Agent Sandbox

Python Client

This Python client provides a simple, high-level interface for creating and interacting with sandboxes managed by the Agent Sandbox controller. It’s designed to be used as a context manager, ensuring that sandbox resources are properly created and cleaned up.

It supports a scalable, cloud-native architecture using Kubernetes Gateways and a specialized Router, while maintaining a convenient Developer Mode for local testing.

Architecture

The client operates in three modes:

1. Production (Gateway Mode): Traffic flows from the Client -> Cloud Load Balancer (Gateway) -> Router Service -> Sandbox Pod. This supports high-scale deployments.
2. Development (Tunnel Mode): Traffic flows from Localhost -> kubectl port-forward -> Router Service -> Sandbox Pod. This requires no public IP and works on Kind/Minikube.
3. Advanced / Internal Mode: The client connects directly to a provided api_url, bypassing discovery. This is useful for in-cluster communication or when connecting through a custom domain.

Prerequisites

• A running Kubernetes cluster.
• The Agent Sandbox Controller installed.
• kubectl installed and configured locally.

Setup: Deploying the Router

Before using the client, you must deploy the sandbox-router. This is a one-time setup.

1. Build and Push the Router Image:

   For both Gateway Mode and Tunnel Mode, follow the instructions in sandbox-router to build, push, and apply the router image and resources.

2. Create a Sandbox Template:

   Ensure a SandboxTemplate exists in your target namespace. The test_client.py uses the python-runtime-sandbox image.

   kubectl apply -f python-sandbox-template.yaml
   

Installation

1. Create a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate
   

2. Install Agent Sandbox Client

   • Option 1: Install from PyPI (Recommended):

     The package is available on PyPI as k8s-agent-sandbox.

     pip install k8s-agent-sandbox
     

     If you are using tracing with GCP, install with the optional tracing dependencies:

     pip install "k8s-agent-sandbox[tracing]"
     

   • Option 2: Install from source via git:

     # Replace "main" with a specific version tag (e.g., "v0.1.0") from
     # https://github.com/kubernetes-sigs/agent-sandbox/releases to pin a version tag.
     export VERSION="main"
     
     pip install "git+https://github.com/kubernetes-sigs/agent-sandbox.git@${VERSION}#subdirectory=clients/python/agentic-sandbox-client"
     

     Note: This package uses setuptools-scm for dynamic versioning. For Option 2 and Option 3, when installing locally, you may notice the version increment if your local repository has uncommitted changes or is ahead of the last tagged release. This is expected behavior to ensure unique versioning during development.

   • Option 3: Install from source in editable mode:

     If you have not already done so, first clone this repository:

     cd ~
     git clone https://github.com/kubernetes-sigs/agent-sandbox.git
     cd agent-sandbox/clients/python/agentic-sandbox-client
     

     And then install the agentic-sandbox-client into your activated .venv:

     pip install -e .
     

     If you are using tracing with GCP, install with the optional tracing dependencies:

     pip install -e ".[tracing]"
     

Usage Examples

1. Production Mode (GKE Gateway)

Use this when running against a real cluster with a public Gateway IP. The client automatically discovers the Gateway.

from k8s_agent_sandbox import SandboxClient
from k8s_agent_sandbox.models import SandboxGatewayConnectionConfig

# Connect via the GKE Gateway
client = SandboxClient(
    connection_config=SandboxGatewayConnectionConfig(
        gateway_name="external-http-gateway",  # Name of the Gateway resource
    )
)

sandbox = client.create_sandbox(template="python-sandbox-template", namespace="default")
try:
    print(sandbox.commands.run("echo 'Hello from Cloud!'").stdout)
finally:
    sandbox.terminate()

2. Developer Mode (Local Tunnel)

Use this for local development or CI. The client automatically opens a secure tunnel to the Router Service using kubectl.

from k8s_agent_sandbox import SandboxClient
from k8s_agent_sandbox.models import SandboxLocalTunnelConnectionConfig

# Automatically tunnels to svc/sandbox-router-svc
client = SandboxClient(
    connection_config=SandboxLocalTunnelConnectionConfig()
)

sandbox = client.create_sandbox(template="python-sandbox-template", namespace="default")
try:
    print(sandbox.commands.run("echo 'Hello from Local!'").stdout)
finally:
    sandbox.terminate()

3. Advanced / Internal Mode

Use SandboxDirectConnectionConfig to bypass discovery entirely. Useful for:

• Internal Agents: Running inside the cluster (connect via K8s DNS).
• Custom Domains: Connecting via HTTPS (e.g., https://sandbox.example.com).

from k8s_agent_sandbox import SandboxClient
from k8s_agent_sandbox.models import SandboxDirectConnectionConfig

client = SandboxClient(
    connection_config=SandboxDirectConnectionConfig(
       api_url="http://sandbox-router-svc.default.svc.cluster.local:8080"
    )
)

sandbox = client.create_sandbox(template="python-sandbox-template", namespace="default")
try:
    sandbox.commands.run("ls -la")
finally:
    sandbox.terminate()

4. Custom Ports

If your sandbox runtime listens on a port other than 8888 (e.g., a Node.js app on 3000), specify server_port.

from k8s_agent_sandbox import SandboxClient
from k8s_agent_sandbox.models import SandboxLocalTunnelConnectionConfig

client = SandboxClient(
    connection_config=SandboxLocalTunnelConnectionConfig(server_port=3000)
)

sandbox = client.create_sandbox(template="node-sandbox-template", namespace="default").

Testing

A test script is included to verify the full lifecycle (Creation -> Execution -> File I/O -> Cleanup).

Run in Dev Mode:

python test_client.py --namespace default

Run in Production Mode:

python test_client.py --gateway-name external-http-gateway