k8s-agent-sandbox 0.1.1.post2

A client library to interact with the Agentic Sandbox on Kubernetes.

Agentic Sandbox Client Python

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 two 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. Option 1: 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"
   

3. Option 2: 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 agentic_sandbox import SandboxClient

# Connect via the GKE Gateway
with SandboxClient(
    template_name="python-sandbox-template",
    gateway_name="external-http-gateway",  # Name of the Gateway resource
    namespace="default"
) as sandbox:
    print(sandbox.run("echo 'Hello from Cloud!'").stdout)

2. Developer Mode (Local Tunnel)

Use this for local development or CI. If you omit gateway_name, the client automatically opens a secure tunnel to the Router Service using kubectl.

from agentic_sandbox import SandboxClient

# Automatically tunnels to svc/sandbox-router-svc
with SandboxClient(
    template_name="python-sandbox-template",
    namespace="default"
) as sandbox:
    print(sandbox.run("echo 'Hello from Local!'").stdout)

3. Advanced / Internal Mode

Use api_url 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).

with SandboxClient(
    template_name="python-sandbox-template",
    # Connect directly to a URL
    api_url="http://sandbox-router-svc.default.svc.cluster.local:8080",
    namespace="default"
) as sandbox:
    sandbox.run("ls -la")

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.

with SandboxClient(
    template_name="node-sandbox-template",
    server_port=3000
) as sandbox:
    # ...

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