> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openhands.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Docker Sandbox

> Run agent server in isolated Docker containers for security and reproducibility.

export const path_to_script_2 = "examples/02_remote_agent_server/03_browser_use_with_docker_sandboxed_server.py"

export const path_to_script_1 = "examples/02_remote_agent_server/05_vscode_with_docker_sandboxed_server.py"

export const path_to_script_0 = "examples/02_remote_agent_server/02_convo_with_docker_sandboxed_server.py"

The docker sandboxed agent server demonstrates how to run agents in isolated Docker containers using `DockerWorkspace`.

This provides complete isolation from the host system, making it ideal for production deployments, testing, and executing untrusted code safely.

Use `DockerWorkspace` with a pre-built agent server image for the fastest startup. When you need to build your own image from a base image, switch to `DockerDevWorkspace`.

<Note>the Docker sandbox image ships with features configured in the [Dockerfile](https://github.com/OpenHands/software-agent-sdk/blob/main/openhands-agent-server/openhands/agent_server/docker/Dockerfile) (e.g., secure defaults and services like VSCode and VNC exposed behind well-defined ports), which are not available in the local (non-Docker) agent server.</Note>

## 1) Basic Docker Sandbox

> A ready-to-run example is available [here](#ready-to-run-example-docker-sandbox)!

### Key Concepts

#### DockerWorkspace Context Manager

The `DockerWorkspace` uses a context manager to automatically handle container lifecycle:

```python icon="python" theme={null}
with DockerWorkspace(
    # use pre-built image for faster startup (recommended)
    server_image="ghcr.io/openhands/agent-server:latest-python",
    host_port=8010,
    platform=detect_platform(),
) as workspace:
    # Container is running here
    # Work with the workspace
    pass
# Container is automatically stopped and cleaned up here
```

The workspace automatically:

* Pulls or builds the Docker image
* Starts the container with an agent server
* Waits for the server to be ready
* Cleans up the container when done

#### Platform Detection

The example includes platform detection to ensure the correct Docker image is built and used:

```python icon="python" theme={null}
def detect_platform():
    """Detects the correct Docker platform string."""
    machine = platform.machine().lower()
    if "arm" in machine or "aarch64" in machine:
        return "linux/arm64"
    return "linux/amd64"
```

This ensures compatibility across different CPU architectures (Intel/AMD vs ARM/Apple Silicon).

#### Testing the Workspace

Before creating a conversation, the example tests the workspace connection:

```python icon="python" theme={null}
result = workspace.execute_command(
    "echo 'Hello from sandboxed environment!' && pwd"
)
logger.info(
    f"Command '{result.command}' completed"
    f"with exit code {result.exit_code}"
)
logger.info(f"Output: {result.stdout}")
```

This verifies the workspace is properly initialized and can execute commands.

#### Automatic RemoteConversation

When you use a DockerWorkspace, the Conversation automatically becomes a RemoteConversation:

```python icon="python" focus={1, 3, 7} theme={null}
conversation = Conversation(
    agent=agent,
    workspace=workspace,
    callbacks=[event_callback],
    visualize=True,
)
assert isinstance(conversation, RemoteConversation)
```

The SDK detects the remote workspace and uses WebSocket communication for real-time event streaming.

#### DockerWorkspace vs DockerDevWorkspace

Use `DockerWorkspace` when you can rely on the official pre-built images for the agent server. Switch to `DockerDevWorkspace` when you need to build or customize the image on-demand (slower startup, requires the SDK source tree and Docker build support).

```python icon="python" theme={null}
# ✅ Fast: Use pre-built image (recommended)
DockerWorkspace(
    server_image="ghcr.io/openhands/agent-server:latest-python",
    host_port=8010,
)

# 🛠️ Custom: Build on the fly (requires SDK tooling)
DockerDevWorkspace(
    base_image="nikolaik/python-nodejs:python3.12-nodejs22",
    host_port=8010,
    target="source",
)
```

### Ready-tu-run Example Docker Sandbox

<Note>
  This example is available on GitHub: [examples/02\_remote\_agent\_server/02\_convo\_with\_docker\_sandboxed\_server.py](https://github.com/OpenHands/software-agent-sdk/blob/main/examples/02_remote_agent_server/02_convo_with_docker_sandboxed_server.py)
</Note>

This example shows how to create a DockerWorkspace that automatically manages Docker containers for agent execution:

```python icon="python" expandable examples/02_remote_agent_server/02_convo_with_docker_sandboxed_server.py theme={null}
import os
import platform
import time

from pydantic import SecretStr

from openhands.sdk import (
    LLM,
    Conversation,
    RemoteConversation,
    get_logger,
)
from openhands.tools.preset.default import get_default_agent
from openhands.workspace import DockerWorkspace


logger = get_logger(__name__)

# 1) Ensure we have LLM API key
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."

llm = LLM(
    usage_id="agent",
    model=os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929"),
    base_url=os.getenv("LLM_BASE_URL"),
    api_key=SecretStr(api_key),
)


def detect_platform():
    """Detects the correct Docker platform string."""
    machine = platform.machine().lower()
    if "arm" in machine or "aarch64" in machine:
        return "linux/arm64"
    return "linux/amd64"


def get_server_image():
    """Get the server image tag, using PR-specific image in CI."""
    platform_str = detect_platform()
    arch = "arm64" if "arm64" in platform_str else "amd64"
    # If GITHUB_SHA is set (e.g. running in CI of a PR), use that to ensure consistency
    # Otherwise, use the latest image from main
    github_sha = os.getenv("GITHUB_SHA")
    if github_sha:
        return f"ghcr.io/openhands/agent-server:{github_sha[:7]}-python-{arch}"
    return "ghcr.io/openhands/agent-server:latest-python"


# 2) Create a Docker-based remote workspace that will set up and manage
#    the Docker container automatically. Use `DockerWorkspace` with a pre-built
#    image or `DockerDevWorkspace` to automatically build the image on-demand.
#    with DockerDevWorkspace(
#        # dynamically build agent-server image
#        base_image="nikolaik/python-nodejs:python3.13-nodejs22",
#        host_port=8010,
#        platform=detect_platform(),
#    ) as workspace:
server_image = get_server_image()
logger.info(f"Using server image: {server_image}")
with DockerWorkspace(
    # use pre-built image for faster startup
    server_image=server_image,
    host_port=8010,
    platform=detect_platform(),
) as workspace:
    # 3) Create agent
    agent = get_default_agent(
        llm=llm,
        cli_mode=True,
    )

    # 4) Set up callback collection
    received_events: list = []
    last_event_time = {"ts": time.time()}

    def event_callback(event) -> None:
        event_type = type(event).__name__
        logger.info(f"🔔 Callback received event: {event_type}\n{event}")
        received_events.append(event)
        last_event_time["ts"] = time.time()

    # 5) Test the workspace with a simple command
    result = workspace.execute_command(
        "echo 'Hello from sandboxed environment!' && pwd"
    )
    logger.info(
        f"Command '{result.command}' completed with exit code {result.exit_code}"
    )
    logger.info(f"Output: {result.stdout}")
    conversation = Conversation(
        agent=agent,
        workspace=workspace,
        callbacks=[event_callback],
    )
    assert isinstance(conversation, RemoteConversation)

    try:
        logger.info(f"\n📋 Conversation ID: {conversation.state.id}")

        logger.info("📝 Sending first message...")
        conversation.send_message(
            "Read the current repo and write 3 facts about the project into FACTS.txt."
        )
        logger.info("🚀 Running conversation...")
        conversation.run()
        logger.info("✅ First task completed!")
        logger.info(f"Agent status: {conversation.state.execution_status}")

        # Wait for events to settle (no events for 2 seconds)
        logger.info("⏳ Waiting for events to stop...")
        while time.time() - last_event_time["ts"] < 2.0:
            time.sleep(0.1)
        logger.info("✅ Events have stopped")

        logger.info("🚀 Running conversation again...")
        conversation.send_message("Great! Now delete that file.")
        conversation.run()
        logger.info("✅ Second task completed!")

        cost = conversation.conversation_stats.get_combined_metrics().accumulated_cost
        print(f"EXAMPLE_COST: {cost}")
    finally:
        print("\n🧹 Cleaning up conversation...")
        conversation.close()
```

You can run the example code as-is.

<Note>
  The model name should follow the [LiteLLM convention](https://models.litellm.ai/): `provider/model_name` (e.g., `anthropic/claude-sonnet-4-5-20250929`, `openai/gpt-4o`).
  The `LLM_API_KEY` should be the API key for your chosen provider.
</Note>

<CodeGroup>
  <CodeBlock language="bash" filename="Bring-your-own provider key" icon="terminal" wrap>
    {`export LLM_API_KEY="your-api-key"\nexport LLM_MODEL="anthropic/claude-sonnet-4-5-20250929"  # or openai/gpt-4o, etc.\ncd software-agent-sdk\nuv run python ${path_to_script_0}`}
  </CodeBlock>

  <CodeBlock language="bash" filename="OpenHands Cloud" icon="terminal" wrap>
    {`# https://app.all-hands.dev/settings/api-keys\nexport LLM_API_KEY="your-openhands-api-key"\nexport LLM_MODEL="openhands/claude-sonnet-4-5-20250929"\ncd software-agent-sdk\nuv run python ${path_to_script_0}`}
  </CodeBlock>
</CodeGroup>

<Tip>
  **ChatGPT Plus/Pro subscribers**: You can use `LLM.subscription_login()` to authenticate with your ChatGPT account and access Codex models without consuming API credits. See the [LLM Subscriptions guide](/sdk/guides/llm-subscriptions) for details.
</Tip>

***

## 2) VS Code in Docker Sandbox

> A ready-to-run example is available [here](#ready-to-run-example-vs-code)!

VS Code with Docker demonstrates how to enable VS Code Web integration in a Docker-sandboxed environment. This allows you to access a full VS Code editor running in the container, making it easy to inspect, edit, and manage files that the agent is working with.

### Key Concepts

#### VS Code-Enabled DockerWorkspace

The workspace is configured with extra ports for VS Code access:

```python icon="python" focus={1, 5} theme={null}
with DockerWorkspace(
    server_image="ghcr.io/openhands/agent-server:latest-python",
    host_port=18010,
    platform="linux/arm64", # or "linux/amd64" depending on your architecture
    extra_ports=True,  # Expose extra ports for VSCode and VNC
) as workspace:
    """Extra ports allows you to access VSCode at localhost:18011"""
```

The `extra_ports=True` setting exposes:

* Port `host_port+1`: VS Code Web interface (host\_port + 1)
* Port `host_port+2`: VNC viewer for visual access

If you need to customize the agent-server image, swap in `DockerDevWorkspace` with the same parameters and provide `base_image`/`target` to build on demand.

#### VS Code URL Generation

The example retrieves the VS Code URL with authentication token:

```python icon="python" theme={null}
# Get VSCode URL with token
vscode_port = (workspace.host_port or 8010) + 1
try:
    response = httpx.get(
        f"{workspace.host}/api/vscode/url",
        params={"workspace_dir": workspace.working_dir},
    )
    vscode_data = response.json()
    vscode_url = vscode_data.get("url", "").replace(
        "localhost:8001", f"localhost:{vscode_port}"
    )
except Exception:
    # Fallback if server route not available
    folder = (
        f"/{workspace.working_dir}"
        if not str(workspace.working_dir).startswith("/")
        else str(workspace.working_dir)
    )
    vscode_url = f"http://localhost:{vscode_port}/?folder={folder}"
```

This generates a properly authenticated URL with the workspace directory pre-opened.

#### VS Code URL Format

```text theme={null}
http://localhost:{vscode_port}/?tkn={token}&folder={workspace_dir}
```

where:

* `vscode_port`: Usually host\_port + 1 (e.g., 8011)
* `token`: Authentication token for security
* `workspace_dir`: Workspace directory to open

### Ready-to-run Example VS Code

<Note>
  This example is available on GitHub: [examples/02\_remote\_agent\_server/05\_vscode\_with\_docker\_sandboxed\_server.py](https://github.com/OpenHands/software-agent-sdk/blob/main/examples/02_remote_agent_server/05_vscode_with_docker_sandboxed_server.py)
</Note>

```python icon="python" expandable examples/02_remote_agent_server/05_vscode_with_docker_sandboxed_server.py theme={null}
import os
import platform
import time

import httpx
from pydantic import SecretStr

from openhands.sdk import LLM, Conversation, get_logger
from openhands.sdk.conversation.impl.remote_conversation import RemoteConversation
from openhands.tools.preset.default import get_default_agent
from openhands.workspace import DockerWorkspace


logger = get_logger(__name__)

api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."

llm = LLM(
    usage_id="agent",
    model=os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929"),
    base_url=os.getenv("LLM_BASE_URL"),
    api_key=SecretStr(api_key),
)


# Create a Docker-based remote workspace with extra ports for VSCode access
def detect_platform():
    """Detects the correct Docker platform string."""
    machine = platform.machine().lower()
    if "arm" in machine or "aarch64" in machine:
        return "linux/arm64"
    return "linux/amd64"


def get_server_image():
    """Get the server image tag, using PR-specific image in CI."""
    platform_str = detect_platform()
    arch = "arm64" if "arm64" in platform_str else "amd64"
    # If GITHUB_SHA is set (e.g. running in CI of a PR), use that to ensure consistency
    # Otherwise, use the latest image from main
    github_sha = os.getenv("GITHUB_SHA")
    if github_sha:
        return f"ghcr.io/openhands/agent-server:{github_sha[:7]}-python-{arch}"
    return "ghcr.io/openhands/agent-server:latest-python"


server_image = get_server_image()
logger.info(f"Using server image: {server_image}")
with DockerWorkspace(
    server_image=server_image,
    host_port=18010,
    platform=detect_platform(),
    extra_ports=True,  # Expose extra ports for VSCode and VNC
) as workspace:
    """Extra ports allows you to access VSCode at localhost:18011"""

    # Create agent
    agent = get_default_agent(
        llm=llm,
        cli_mode=True,
    )

    # Set up callback collection
    received_events: list = []
    last_event_time = {"ts": time.time()}

    def event_callback(event) -> None:
        event_type = type(event).__name__
        logger.info(f"🔔 Callback received event: {event_type}\n{event}")
        received_events.append(event)
        last_event_time["ts"] = time.time()

    # Create RemoteConversation using the workspace
    conversation = Conversation(
        agent=agent,
        workspace=workspace,
        callbacks=[event_callback],
    )
    assert isinstance(conversation, RemoteConversation)

    logger.info(f"\n📋 Conversation ID: {conversation.state.id}")
    logger.info("📝 Sending first message...")
    conversation.send_message("Create a simple Python script that prints Hello World")
    conversation.run()

    # Get VSCode URL with token
    vscode_port = (workspace.host_port or 8010) + 1
    try:
        response = httpx.get(
            f"{workspace.host}/api/vscode/url",
            params={"workspace_dir": workspace.working_dir},
        )
        vscode_data = response.json()
        vscode_url = vscode_data.get("url", "").replace(
            "localhost:8001", f"localhost:{vscode_port}"
        )
    except Exception:
        # Fallback if server route not available
        folder = (
            f"/{workspace.working_dir}"
            if not str(workspace.working_dir).startswith("/")
            else str(workspace.working_dir)
        )
        vscode_url = f"http://localhost:{vscode_port}/?folder={folder}"

    # Wait for user to explore VSCode
    y = None
    while y != "y":
        y = input(
            "\n"
            "Because you've enabled extra_ports=True in DockerDevWorkspace, "
            "you can open VSCode Web to see the workspace.\n\n"
            f"VSCode URL: {vscode_url}\n\n"
            "The VSCode should have the OpenHands settings extension installed:\n"
            "  - Dark theme enabled\n"
            "  - Auto-save enabled\n"
            "  - Telemetry disabled\n"
            "  - Auto-updates disabled\n\n"
            "Press 'y' and Enter to exit and terminate the workspace.\n"
            ">> "
        )
```

You can run the example code as-is.

<Note>
  The model name should follow the [LiteLLM convention](https://models.litellm.ai/): `provider/model_name` (e.g., `anthropic/claude-sonnet-4-5-20250929`, `openai/gpt-4o`).
  The `LLM_API_KEY` should be the API key for your chosen provider.
</Note>

<CodeGroup>
  <CodeBlock language="bash" filename="Bring-your-own provider key" icon="terminal" wrap>
    {`export LLM_API_KEY="your-api-key"\nexport LLM_MODEL="anthropic/claude-sonnet-4-5-20250929"  # or openai/gpt-4o, etc.\ncd software-agent-sdk\nuv run python ${path_to_script_1}`}
  </CodeBlock>

  <CodeBlock language="bash" filename="OpenHands Cloud" icon="terminal" wrap>
    {`# https://app.all-hands.dev/settings/api-keys\nexport LLM_API_KEY="your-openhands-api-key"\nexport LLM_MODEL="openhands/claude-sonnet-4-5-20250929"\ncd software-agent-sdk\nuv run python ${path_to_script_1}`}
  </CodeBlock>
</CodeGroup>

<Tip>
  **ChatGPT Plus/Pro subscribers**: You can use `LLM.subscription_login()` to authenticate with your ChatGPT account and access Codex models without consuming API credits. See the [LLM Subscriptions guide](/sdk/guides/llm-subscriptions) for details.
</Tip>

***

## 3) Browser in Docker Sandbox

> A ready-to-run example is available [here](#ready-to-run-example-browser)!

Browser with Docker demonstrates how to enable browser automation capabilities in a Docker-sandboxed environment. This allows agents to browse websites, interact with web content, and perform web automation tasks while maintaining complete isolation from your host system.

### Key Concepts

#### Browser-Enabled DockerWorkspace

The workspace is configured with extra ports for browser access:

```python icon="python" focus={1-5} theme={null}
with DockerWorkspace(
    server_image="ghcr.io/openhands/agent-server:latest-python",
    host_port=8010,
    platform=detect_platform(),
    extra_ports=True,  # Expose extra ports for VSCode and VNC
) as workspace:
    """Extra ports allows you to check localhost:8012 for VNC"""
```

The `extra_ports=True` setting exposes additional ports for:

* Port `host_port+1`: VS Code Web interface
* Port `host_port+2`: VNC viewer for browser visualization

If you need to pre-build a custom browser image, replace `DockerWorkspace` with `DockerDevWorkspace` and provide `base_image`/`target` to build before launch.

#### Enabling Browser Tools

Browser tools are enabled by setting `cli_mode=False`:

```python icon="python" focus={2, 4} theme={null}
# Create agent with browser tools enabled
agent = get_default_agent(
    llm=llm,
    cli_mode=False,  # CLI mode = False will enable browser tools
)
```

When `cli_mode=False`, the agent gains access to browser automation tools for web interaction.

When VNC is available and `extra_ports=True`, the browser will be opened in the VNC desktop to visualize agent's work. You can watch the browser in real-time via VNC. Demo video:

<video controls className="w-full aspect-video rounded-xl" src="https://github.com/user-attachments/assets/2cd5d08a-043e-4ce1-9d10-5ab1289faa12" />

#### VNC Access

The VNC interface provides real-time visual access to the browser:

```text theme={null}
http://localhost:8012/vnc.html?autoconnect=1&resize=remote
```

* `autoconnect=1`: Automatically connect to VNC server
* `resize=remote`: Automatically adjust resolution

***

### Ready-to-run Example Browser

<Note>
  This example is available on GitHub: [examples/02\_remote\_agent\_server/03\_browser\_use\_with\_docker\_sandboxed\_server.py](https://github.com/OpenHands/software-agent-sdk/blob/main/examples/02_remote_agent_server/03_browser_use_with_docker_sandboxed_server.py)
</Note>

This example shows how to configure `DockerWorkspace` with browser capabilities and VNC access:

```python icon="python" expandable examples/02_remote_agent_server/03_browser_use_with_docker_sandboxed_server.py theme={null}
import os
import platform
import time

from pydantic import SecretStr

from openhands.sdk import LLM, Conversation, get_logger
from openhands.sdk.conversation.impl.remote_conversation import RemoteConversation
from openhands.tools.preset.default import get_default_agent
from openhands.workspace import DockerWorkspace


logger = get_logger(__name__)

api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."

llm = LLM(
    usage_id="agent",
    model=os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929"),
    base_url=os.getenv("LLM_BASE_URL"),
    api_key=SecretStr(api_key),
)


def detect_platform():
    """Detects the correct Docker platform string."""
    machine = platform.machine().lower()
    if "arm" in machine or "aarch64" in machine:
        return "linux/arm64"
    return "linux/amd64"


def get_server_image():
    """Get the server image tag, using PR-specific image in CI."""
    platform_str = detect_platform()
    arch = "arm64" if "arm64" in platform_str else "amd64"
    # If GITHUB_SHA is set (e.g. running in CI of a PR), use that to ensure consistency
    # Otherwise, use the latest image from main
    github_sha = os.getenv("GITHUB_SHA")
    if github_sha:
        return f"ghcr.io/openhands/agent-server:{github_sha[:7]}-python-{arch}"
    return "ghcr.io/openhands/agent-server:latest-python"


# Create a Docker-based remote workspace with extra ports for browser access.
# Use `DockerWorkspace` with a pre-built image or `DockerDevWorkspace` to
# automatically build the image on-demand.
#    with DockerDevWorkspace(
#        # dynamically build agent-server image
#        base_image="nikolaik/python-nodejs:python3.13-nodejs22",
#        host_port=8010,
#        platform=detect_platform(),
#    ) as workspace:
server_image = get_server_image()
logger.info(f"Using server image: {server_image}")
with DockerWorkspace(
    server_image=server_image,
    host_port=8011,
    platform=detect_platform(),
    extra_ports=True,  # Expose extra ports for VSCode and VNC
) as workspace:
    """Extra ports allows you to check localhost:8012 for VNC"""

    # Create agent with browser tools enabled
    agent = get_default_agent(
        llm=llm,
        cli_mode=False,  # CLI mode = False will enable browser tools
    )

    # Set up callback collection
    received_events: list = []
    last_event_time = {"ts": time.time()}

    def event_callback(event) -> None:
        event_type = type(event).__name__
        logger.info(f"🔔 Callback received event: {event_type}\n{event}")
        received_events.append(event)
        last_event_time["ts"] = time.time()

    # Create RemoteConversation using the workspace
    conversation = Conversation(
        agent=agent,
        workspace=workspace,
        callbacks=[event_callback],
    )
    assert isinstance(conversation, RemoteConversation)

    logger.info(f"\n📋 Conversation ID: {conversation.state.id}")
    logger.info("📝 Sending first message...")
    conversation.send_message(
        "Could you go to https://openhands.dev/ blog page and summarize main "
        "points of the latest blog?"
    )
    conversation.run()

    cost = conversation.conversation_stats.get_combined_metrics().accumulated_cost
    print(f"EXAMPLE_COST: {cost}")

    if os.getenv("CI"):
        logger.info(
            "CI environment detected; skipping interactive prompt and closing workspace."  # noqa: E501
        )
    else:
        # Wait for user confirm to exit when running locally
        y = None
        while y != "y":
            y = input(
                "Because you've enabled extra_ports=True in DockerDevWorkspace, "
                "you can open a browser tab to see the *actual* browser OpenHands "
                "is interacting with via VNC.\n\n"
                "Link: http://localhost:8012/vnc.html?autoconnect=1&resize=remote\n\n"
                "Press 'y' and Enter to exit and terminate the workspace.\n"
                ">> "
            )
```

You can run the example code as-is.

<Note>
  The model name should follow the [LiteLLM convention](https://models.litellm.ai/): `provider/model_name` (e.g., `anthropic/claude-sonnet-4-5-20250929`, `openai/gpt-4o`).
  The `LLM_API_KEY` should be the API key for your chosen provider.
</Note>

<CodeGroup>
  <CodeBlock language="bash" filename="Bring-your-own provider key" icon="terminal" wrap>
    {`export LLM_API_KEY="your-api-key"\nexport LLM_MODEL="anthropic/claude-sonnet-4-5-20250929"  # or openai/gpt-4o, etc.\ncd software-agent-sdk\nuv run python ${path_to_script_2}`}
  </CodeBlock>

  <CodeBlock language="bash" filename="OpenHands Cloud" icon="terminal" wrap>
    {`# https://app.all-hands.dev/settings/api-keys\nexport LLM_API_KEY="your-openhands-api-key"\nexport LLM_MODEL="openhands/claude-sonnet-4-5-20250929"\ncd software-agent-sdk\nuv run python ${path_to_script_2}`}
  </CodeBlock>
</CodeGroup>

<Tip>
  **ChatGPT Plus/Pro subscribers**: You can use `LLM.subscription_login()` to authenticate with your ChatGPT account and access Codex models without consuming API credits. See the [LLM Subscriptions guide](/sdk/guides/llm-subscriptions) for details.
</Tip>

## Next Steps

* **[Local Agent Server](/sdk/guides/agent-server/local-server)**
* **[Agent Server Overview](/sdk/guides/agent-server/overview)** - Architecture and implementation details
* **[API Sandboxed Server](/sdk/guides/agent-server/api-sandbox)** - Connect to hosted API service
* **[Agent Server Package Architecture](/sdk/arch/agent-server)** - Remote execution architecture