obsidian/raw/_processed/Quickstart.md

title	source	author	published	created	description	tags
Quickstart	https://code.claude.com/docs/en/agent-sdk/quickstart			2026-04-17	Get started with the Python or TypeScript Agent SDK to build AI agents that work autonomously	clippings

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:

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
• TypeScript

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.

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:

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:

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.

Troubleshooting

API error thinking.type.enabled is not supported for this model

Claude Opus 4.7 replaces thinking.type.enabled with thinking.type.adaptive. Older Agent SDK versions fail with the following API error when you select claude-opus-4-7:

API Error: 400 {"type":"invalid_request_error","message":"\"thinking.type.enabled\" is not supported for this model. Use \"thinking.type.adaptive\" and \"output_config.effort\" to control thinking behavior."}

Upgrade to Agent SDK v0.2.111 or later to use Opus 4.7.

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