Quickstart

Get started with the Python or TypeScript Agent SDK to build AI agents that work autonomously

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

1. Create a project folder

   Create a new directory for this quickstart:

   mkdir my-agent && cd my-agent
   

   For your own projects, you can run the SDK from any folder; it will have access to files in that directory and its subdirectories by default.

2. Install the SDK

   Install the Agent SDK package for your language:

   TypeScript

   npm install @anthropic-ai/claude-agent-sdk
   

   Python (uv)

   uv Python package manager is a fast Python package manager that handles virtual environments automatically:

   uv init && uv add claude-agent-sdk
   

   Python (pip)

   Create a virtual environment first, then install:

   python3 -m venv .venv && source .venv/bin/activate
   pip3 install claude-agent-sdk
   
3. Set your API key

   Get an API key from the Claude Console, then create a .env file in your project directory:

   ANTHROPIC_API_KEY=your-api-key
   

   The SDK also supports authentication via third-party API providers:

   • Amazon Bedrock: set CLAUDE_CODE_USE_BEDROCK=1 environment variable and configure AWS credentials
   • Google Vertex AI: set CLAUDE_CODE_USE_VERTEX=1 environment variable and configure Google Cloud credentials
   • Microsoft Azure: set CLAUDE_CODE_USE_FOUNDRY=1 environment variable and configure Azure credentials

   See the setup guides for Bedrock, Vertex AI, or Azure AI Foundry for details.

   Unless previously approved, Anthropic does not allow third party developers to offer claude.ai login or rate limits for their products, including agents built on the Claude Agent SDK. Please use the API key authentication methods described in this document instead.

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:

Python

import asyncio
from claude_agent_sdk import query, ClaudeAgentOptions, AssistantMessage, ResultMessage


async def main():
    # Agentic loop: streams messages as Claude works
    async for message in query(
        prompt="Review utils.py for bugs that would cause crashes. Fix any issues you find.",
        options=ClaudeAgentOptions(
            allowed_tools=["Read", "Edit", "Glob"],  # Tools Claude can use
            permission_mode="acceptEdits",  # Auto-approve file edits
        ),
    ):
        # Print human-readable output
        if isinstance(message, AssistantMessage):
            for block in message.content:
                if hasattr(block, "text"):
                    print(block.text)  # Claude's reasoning
                elif hasattr(block, "name"):
                    print(f"Tool: {block.name}")  # Tool being called
        elif isinstance(message, ResultMessage):
            print(f"Done: {message.subtype}")  # Final result


asyncio.run(main())

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 pre-approve 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.

This example uses streaming to show progress in real-time. If you don’t need live output (e.g., for background jobs or CI pipelines), you can collect all messages at once. See Streaming vs. single-turn mode for details.

Run your agent

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

Python

python3 agent.py

TypeScript

npx tsx agent.ts

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.

If you see “API key not found”, make sure you’ve set the ANTHROPIC_API_KEY environment variable in your .env file or shell environment. See the full troubleshooting guide for more help.

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:

Python

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

Give Claude a custom system prompt:

Python

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:

Python

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:

Tools	What the agent can do
Read, Glob, Grep	Read-only analysis
Read, Edit, Glob	Analyze and modify code
Read, Edit, Bash, Glob, Grep	Full automation

Permission modes control how much human oversight you want:

Mode	Behavior	Use case
acceptEdits	Auto-approves file edits and common filesystem commands, asks for other actions	Trusted development workflows
dontAsk	Denies anything not in allowedTools	Locked-down headless agents
auto (TypeScript only)	A model classifier approves or denies each tool call	Autonomous agents with safety guardrails
bypassPermissions	Runs every tool without prompts	Sandboxed CI, fully trusted environments
default	Requires a canUseTool callback to handle approval	Custom approval flows

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:

• 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