arl-env 0.15.4

ARL Infrastructure - Python SDK for Kubernetes-based Agent Runtime Layer

arl-env Python SDK

High-level Python SDK for the agent-env Gateway API. The SDK creates sandbox sessions, runs commands, streams output, transfers files, opens interactive shells, and manages SandboxWarmPool resources through the gateway.

Installation

pip install arl-env
# or
uv add arl-env

Interactive shell support needs the optional dependency:

pip install "arl-env[shell]"

Authentication

If gateway authentication is enabled, provide a bearer API key through the environment or constructor:

export ARL_API_KEY="your-api-key"

from arl import SandboxSession

session = SandboxSession(
    image="python:3.12",
    profile="python-pool",
    gateway_url="http://localhost:8080",
    api_key="your-api-key",
)

Basic Usage

from arl import SandboxSession

with SandboxSession(
    image="python:3.12",
    profile="python-pool",
    namespace="default",
    gateway_url="http://localhost:8080",
) as session:
    result = session.execute([
        {"name": "hello", "command": ["echo", "Hello, World!"]},
    ])
    print(result.results[0].output.stdout)

Commands run in the executor container, which uses the requested image. The sidecar only exposes the gRPC control plane and proxies execution to the executor-agent over a Unix socket.

Persistent Sessions

Use keep_alive=True when several operations should share the same workspace. Always delete the session when finished.

from arl import SandboxSession

session = SandboxSession(
    image="python:3.12",
    profile="python-pool",
    gateway_url="http://localhost:8080",
    keep_alive=True,
)

try:
    session.create_sandbox()
    session.execute([
        {"name": "init", "command": ["sh", "-c", "echo 0 > /workspace/count.txt"]},
    ])
    result = session.execute([
        {"name": "read", "command": ["cat", "/workspace/count.txt"]},
    ])
    print(result.results[0].output.stdout)
finally:
    session.delete_sandbox()
    session.close()

Attach to an existing session:

from arl import SandboxSession

session = SandboxSession.attach("gw-12345", gateway_url="http://localhost:8080")
result = session.execute([{"name": "pwd", "command": ["pwd"]}])
session.close()

Streaming Output

execute() uses the gateway SSE endpoint when available. Pass on_output to receive stdout/stderr chunks while the step is still running.

def on_output(stdout: str, stderr: str) -> None:
    if stdout:
        print(stdout, end="")
    if stderr:
        print(stderr, end="")

result = session.execute(
    [{"name": "loop", "command": ["sh", "-c", "for i in 1 2 3; do echo $i; sleep 1; done"]}],
    on_output=on_output,
)

File Transfer

Paths are relative to the session workspace.

session.upload_file("input.txt", "hello\n")
data = session.download_file("input.txt")

session.upload_path("local.bin", "data/local.bin")
session.download_path("data/local.bin", "out/local.bin")

History, Restore, and Trajectory

Each executed step is recorded in session history. Snapshot IDs are step-index strings used by the gateway's replay-based restore implementation.

r1 = session.execute([{"name": "write", "command": ["sh", "-c", "echo one > /workspace/x"]}])
snapshot_id = r1.results[0].snapshot_id

session.execute([{"name": "change", "command": ["sh", "-c", "echo two > /workspace/x"]}])
session.restore(snapshot_id)

history = session.get_history()
jsonl = session.export_trajectory()

WarmPool Management

WarmPoolManager uses the gateway pool endpoints. Pool creation is an admin operation when gateway auth is enabled.

from arl import ResourceRequirements, WarmPoolManager

manager = WarmPoolManager(namespace="default", gateway_url="http://localhost:8080")
manager.create_warmpool(
    name="python-pool",
    image="python:3.12",
    profile="python-pool",
    replicas=2,
    resources=ResourceRequirements(
        requests={"cpu": "500m", "memory": "512Mi"},
        limits={"cpu": "1", "memory": "1Gi"},
    ),
)
info = manager.wait_for_ready("python-pool", min_ready=1)
print(info.ready_replicas)
manager.scale_warmpool("python-pool", replicas=3)

Current sandbox-backed pools reject tools and config_env provisioning requests. list_tools() and call_tool() only work when the executor image already contains /opt/arl/tools/registry.json and matching tool files.

Managed Sessions

ManagedSession creates or reuses a server-side managed pool for an image and groups sessions by experiment ID.

from arl import ManagedSession

with ManagedSession(
    image="python:3.12",
    experiment_id="exp-1",
    profile="default",
    gateway_url="http://localhost:8080",
) as session:
    result = session.execute([
        {"name": "hello", "command": ["python", "-c", "print('ok')"]},
    ])
    print(result.results[0].output.stdout)

Clean up an experiment:

from arl import GatewayClient

client = GatewayClient(base_url="http://localhost:8080")
deleted = client.delete_experiment("exp-1")

Core Classes

• SandboxSession: session lifecycle, execute, replay, restore, files, logs, history, trajectory.
• ManagedSession: image + experiment session flow with server-side pool creation.
• GatewayClient: low-level HTTP client for all public gateway REST endpoints.
• WarmPoolManager: pool create/list/get/wait/scale/logs/delete helpers.
• InteractiveShellClient: WebSocket shell client.