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. If you don't have one, 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 (new project)

npm init -y
npm pkg set type=module
npm install @anthropic-ai/claude-agent-sdk
npm install --save-dev tsx

Setting "type": "module" in package.json lets your agent script use top-level await, and tsx runs TypeScript files directly. npm prints added N packages when the install succeeds.

TypeScript (existing project)

npm install @anthropic-ai/claude-agent-sdk
npm install --save-dev tsx

tsx runs TypeScript files directly. If your project uses CommonJS, name your agent script agent.mts instead of agent.ts. The .mts extension makes tsx treat the file as an ES module, so top-level await works without converting your whole project to ES modules. Use agent.mts in place of agent.ts in the create and run steps later in this quickstart.

Python (uv)

Install uv, a fast Python package manager that handles virtual environments automatically. Then initialize a project and add the SDK:

uv init
uv add claude-agent-sdk

Python (pip)

Create and activate a virtual environment, then install the package.

On macOS or Linux:

python3 -m venv .venv
source .venv/bin/activate
pip install claude-agent-sdk

On Windows:

py -m venv .venv
.venv\Scripts\Activate.ps1
pip install claude-agent-sdk

If PowerShell blocks Activate.ps1 with an execution policy error, run Set-ExecutionPolicy -Scope Process RemoteSigned first.

[!NOTE]

Both the TypeScript and Python SDKs bundle a native Claude Code binary, so most installs need no separate Claude Code install. Some installs have no bundled binary:

• If pip installs the Python SDK's source distribution instead of a platform wheel, for example on ARM64 Windows, no binary is bundled. Install Claude Code natively. The Python SDK finds it on your PATH.
• The TypeScript SDK installs its binary through npm optional dependencies, so an install that skips them, for example npm ci --omit=optional, gets no binary even on a supported platform. Reinstall without skipping optional dependencies, or install Claude Code natively and set pathToClaudeCodeExecutable to its path.

3. Set your API key

Get an API key from the Claude Console, then set it as an environment variable in the shell where you'll run your agent:

macOS / Linux

export ANTHROPIC_API_KEY=your-api-key

Windows (PowerShell)

$env:ANTHROPIC_API_KEY = "your-api-key"

The SDK reads the key from the environment of the process that runs your agent; it doesn't load .env files automatically. If you keep the key in a .env file, load it yourself, for example with the dotenv package, before calling the SDK.

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

• Amazon Bedrock: set CLAUDE_CODE_USE_BEDROCK=1 environment variable and configure AWS credentials
• Claude Platform on AWS: set CLAUDE_CODE_USE_ANTHROPIC_AWS=1 and ANTHROPIC_AWS_WORKSPACE_ID, then configure AWS credentials
• Google Cloud's Agent Platform: set CLAUDE_CODE_USE_VERTEX=1 environment variable and configure Google Cloud credentials
• Microsoft Foundry: set CLAUDE_CODE_USE_FOUNDRY=1 environment variable and configure Azure credentials

See the setup guides for Amazon Bedrock, Claude Platform on AWS, Google Cloud's Agent Platform, or Microsoft Foundry for details.

[!NOTE]

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. 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. Use agent.mts instead if your existing project uses CommonJS:

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"],  # Auto-approve these tools
            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())

import { query } from "@anthropic-ai/claude-agent-sdk";

// Agentic loop: streams messages as Claude works
for await (const message of query({
  prompt: "Review utils.py for bugs that would cause crashes. Fix any issues you find.",
  options: {
    allowedTools: ["Read", "Edit", "Glob"], // Auto-approve these tools
    permissionMode: "acceptEdits" // Auto-approve file edits
  }
})) {
  // Print human-readable output
  if (message.type === "assistant" && message.message?.content) {
    for (const block of message.message.content) {
      if ("text" in block) {
        console.log(block.text); // Claude's reasoning
      } else if ("name" in block) {
        console.log(`Tool: ${block.name}`); // Tool being called
      }
    }
  } else if (message.type === "result") {
    console.log(`Done: ${message.subtype}`); // Final result
  }
}

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, and retries, so you 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.

[!NOTE]

This example uses streaming to show progress in real-time. If you don't need live output (for example, 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:

TypeScript

npx tsx agent.ts

If you named your script agent.mts, run npx tsx agent.mts instead.

Python (uv)

uv run agent.py

Python (pip)

With your virtual environment still activated:

python agent.py

As it works, the agent prints its reasoning and each tool it calls, ending with Done: success. 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.

[!NOTE]

If you see an authentication error such as Not logged in or Invalid API key, make sure you've set the ANTHROPIC_API_KEY environment variable in the shell where you run your agent. The SDK doesn't load .env files automatically.

For the causes and fixes behind these and other authentication errors, see Authentication errors in the Error reference.

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"
)

const _ = {
  options: {
    allowedTools: ["Read", "Edit", "Glob", "WebSearch"],
    permissionMode: "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.",
)

const _ = {
  options: {
    allowedTools: ["Read", "Edit", "Glob"],
    permissionMode: "acceptEdits",
    systemPrompt: "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"
)

const _ = {
  options: {
    allowedTools: ["Read", "Edit", "Glob", "Bash"],
    permissionMode: "acceptEdits"
  }
};

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

Each of these snippets sets fields on the same options object. For more information, see Configure your agent.

Key concepts

Tools control what your agent can do:

Permission modes control how much human oversight you want. The SDK evaluates the active mode together with your allow and deny rules in a fixed order, described in How permissions are evaluated. For the full list of modes, their behavior, and when to use each, see Permission mode in How the agent loop works.

Next steps

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

• Configure your agent: compose the options object and find the page that covers each setting
• 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
• Troubleshooting: fix errors when the CLI fails to start or exits, or a result arrives without structured output