Quickstart

Use the Agent SDK to build an AI agent that reads your code, finds bugs, and fixes them, all without manual intervention.

What you'll do:

1. Set up a project with the Agent SDK
2. Create a file with some buggy code
3. Run an agent that finds and fixes the bugs automatically

Prerequisites

• Node.js 18+ or Python 3.10+
• An Anthropic account (sign up here)

Setup

Create a buggy file

This quickstart walks you through building an agent that can find and fix bugs in code. First, you need a file with some intentional bugs for the agent to fix. Create utils.py in the my-agent directory and paste the following code:

def calculate_average(numbers):
    total = 0
    for num in numbers:
        total += num
    return total / len(numbers)

def get_user_name(user):
    return user["name"].upper()

This code has two bugs:

1. calculate_average([]) crashes with division by zero
2. get_user_name(None) crashes with a TypeError

Build an agent that finds and fixes bugs

Create agent.py if you're using the Python SDK, or agent.ts for TypeScript:

This code has three main parts:

1. query: the main entry point that creates the agentic loop. It returns an async iterator, so you use async for to stream messages as Claude works. See the full API in the Python or TypeScript SDK reference.

2. prompt: what you want Claude to do. Claude figures out which tools to use based on the task.

3. options: configuration for the agent. This example uses allowedTools to restrict Claude to Read, Edit, and Glob, and permissionMode: "acceptEdits" to auto-approve file changes. Other options include systemPrompt, mcpServers, and more. See all options for Python or TypeScript.

The async for loop keeps running as Claude thinks, calls tools, observes results, and decides what to do next. Each iteration yields a message: Claude's reasoning, a tool call, a tool result, or the final outcome. The SDK handles the orchestration (tool execution, context management, retries) so you just consume the stream. The loop ends when Claude finishes the task or hits an error.

The message handling inside the loop filters for human-readable output. Without filtering, you'd see raw message objects including system initialization and internal state, which is useful for debugging but noisy otherwise.

Run your agent

Your agent is ready. Run it with the following command:

python3 agent.py

After running, check utils.py. You'll see defensive code handling empty lists and null users. Your agent autonomously:

1. Read utils.py to understand the code
2. Analyzed the logic and identified edge cases that would crash
3. Edited the file to add proper error handling

This is what makes the Agent SDK different: Claude executes tools directly instead of asking you to implement them.

Try other prompts

Now that your agent is set up, try some different prompts:

• "Add docstrings to all functions in utils.py"
• "Add type hints to all functions in utils.py"
• "Create a README.md documenting the functions in utils.py"

Customize your agent

You can modify your agent's behavior by changing the options. Here are a few examples:

Add web search capability:

options=ClaudeAgentOptions(
    allowed_tools=["Read", "Edit", "Glob", "WebSearch"],
    permission_mode="acceptEdits"
)

Give Claude a custom system prompt:

options=ClaudeAgentOptions(
    allowed_tools=["Read", "Edit", "Glob"],
    permission_mode="acceptEdits",
    system_prompt="You are a senior Python developer. Always follow PEP 8 style guidelines."
)

Run commands in the terminal:

options=ClaudeAgentOptions(
    allowed_tools=["Read", "Edit", "Glob", "Bash"],
    permission_mode="acceptEdits"
)

With Bash enabled, try: "Write unit tests for utils.py, run them, and fix any failures"

Key concepts

Tools control what your agent can do:

Permission modes control how much human oversight you want:

The example above uses acceptEdits mode, which auto-approves file operations so the agent can run without interactive prompts. If you want to prompt users for approval, use default mode and provide a canUseTool callback that collects user input. For more control, see Permissions.

Next steps

Now that you've created your first agent, learn how to extend its capabilities and tailor it to your use case:

• Available tools: full list of built-in tools your agent can use
• Permissions: control what your agent can do and when it needs approval
• Hooks: run custom code before or after tool calls
• Sessions: build multi-turn agents that maintain context
• MCP servers: connect to databases, browsers, APIs, and other external systems
• Hosting: deploy agents to Docker, cloud, and CI/CD
• Example agents: see complete examples: email assistant, research agent, and more