Quickstart

yes

Editorial Notes

This is the fastest path to a running agent and the best starting point before diving into advanced SDK features. Focus on the AgentConfig pattern, which is the central abstraction for defining agent behavior, tools, and instructions. Pay attention to how the SDK handles the agentic loop automatically – understanding what happens behind the scenes here will save you debugging time later. Read this before the Python or TypeScript language-specific guides so you have the conceptual model in place first.


Original Documentation

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#

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.
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  <span class="step-marker" data-step-title="Install the SDK"></span>
Install the Agent SDK package for your language:

<span class="tab-group-start"></span>
  <span class="tab-start" data-tab-title="TypeScript"></span>
    ```bash
        npm install @anthropic-ai/claude-agent-sdk
        ```
  <span class="tab-end"></span>
  <span class="tab-start" data-tab-title="Python (uv)"></span>
    [uv Python package manager](https://docs.astral.sh/uv/) is a fast Python package manager that handles virtual environments automatically:
    ```bash
        uv init && uv add claude-agent-sdk
        ```
  <span class="tab-end"></span>
  <span class="tab-start" data-tab-title="Python (pip)"></span>
    Create a virtual environment first, then install:
    ```bash
        python3 -m venv .venv && source .venv/bin/activate
        pip3 install claude-agent-sdk
        ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>
  <span class="step-end"></span>

  <span class="step-marker" data-step-title="Set your API key"></span>
Get an API key from the [Claude Console](https://platform.claude.com/), then create a `.env` file in your project directory:

```bash
    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](https://code.claude.com/docs/en/amazon-bedrock), [Vertex AI](https://code.claude.com/docs/en/google-vertex-ai), or [Azure AI Foundry](https://code.claude.com/docs/en/azure-ai-foundry) for details.

<span class="callout-start" data-callout-type="note"></span>
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.
<span class="callout-end"></span>
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## 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:

```python
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())

// 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"], // Tools Claude can use
    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 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.

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:

    python3 agent.py
    ```
  <span class="tab-end"></span>
  <span class="tab-start" data-tab-title="TypeScript"></span>
```bash
    npx tsx agent.ts
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

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.

<span class="callout-start" data-callout-type="note"></span>
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](https://code.claude.com/docs/en/troubleshooting) for more help.
<span class="callout-end"></span>

### 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"
)
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"

Key concepts#

Tools control what your agent can do:

ToolsWhat the agent can do
Read, Glob, GrepRead-only analysis
Read, Edit, GlobAnalyze and modify code
Read, Edit, Bash, Glob, GrepFull automation

Permission modes control how much human oversight you want:

ModeBehaviorUse case
acceptEditsAuto-approves file edits, asks for other actionsTrusted development workflows
bypassPermissionsRuns without promptsCI/CD pipelines, automation
defaultRequires a canUseTool callback to handle approvalCustom 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
Link last verified June 7, 2026. View original ↗
Source: Anthropic Platform Docs

Appears in Learning Paths

Link last verified: 2026-02-26