agentkernel-sdk 0.10.0

Python SDK for agentkernel — run AI coding agents in secure, isolated microVMs

agentkernel

Python SDK for agentkernel — run AI coding agents in secure, isolated microVMs.

Install

pip install agentkernel-sdk

Requires Python 3.10+.

Quick Start

from agentkernel import AgentKernel

with AgentKernel() as client:
    result = client.run(["echo", "hello"])
    print(result.output)  # "hello\n"

Async

from agentkernel import AsyncAgentKernel

async with AsyncAgentKernel() as client:
    result = await client.run(["echo", "hello"])
    print(result.output)

Sandbox Sessions

with AgentKernel() as client:
    with client.sandbox("test", image="python:3.12-alpine") as sb:
        sb.run(["pip", "install", "numpy"])
        result = sb.run(["python3", "-c", "import numpy; print(numpy.__version__)"])
        print(result.output)
    # sandbox auto-removed

Exec Options

Run commands with a working directory, environment variables, or as root:

with AgentKernel() as client:
    result = client.exec_in_sandbox(
        "my-sandbox",
        ["npm", "start"],
        workdir="/app",
        env=["NODE_ENV=production"],
        sudo=True,
    )

Works on sandbox sessions too:

with client.sandbox("dev") as sb:
    sb.run(["pip", "install", "-r", "requirements.txt"], workdir="/app", sudo=True)

Git Source Cloning

Clone a git repo into the sandbox at creation time:

with AgentKernel() as client:
    client.create_sandbox(
        "my-project",
        image="node:20-alpine",
        source_url="https://github.com/user/repo.git",
        source_ref="main",
    )

Persistent Volumes

Mount volumes that persist across sandbox restarts:

# First create volumes via CLI: agentkernel volume create mydata

with AgentKernel() as client:
    client.create_sandbox(
        "my-project",
        image="python:3.12-alpine",
        volumes=["mydata:/data", "cache:/tmp/cache:ro"],
    )

    # Data in /data persists across sandbox restarts
    client.exec_in_sandbox("my-project", ["sh", "-c", "echo hello > /data/test.txt"])

File Operations

Read, write, and delete files in a sandbox:

with AgentKernel() as client:
    # Write a file
    client.write_file("my-sandbox", "app/main.py", "print('hello')")

    # Read a file
    file = client.read_file("my-sandbox", "app/main.py")
    print(file.content)

    # Delete a file
    client.delete_file("my-sandbox", "app/main.py")

    # Batch write multiple files at once
    client.write_files("my-sandbox", {
        "/app/index.js": "console.log('hi')",
        "/app/package.json": '{"name":"app"}',
    })

Detached Commands

Run long-lived processes in the background and retrieve their output later:

with AgentKernel() as client:
    # Start a background process
    cmd = client.exec_detached("my-sandbox", ["python3", "train.py"])
    print(f"Started: {cmd.id} (pid {cmd.pid})")

    # Check status
    status = client.detached_status("my-sandbox", cmd.id)
    print(status.status)  # "running" | "completed" | "failed"

    # Get logs
    logs = client.detached_logs("my-sandbox", cmd.id)
    print(logs.stdout)

    # Get stderr only
    stderr = client.detached_logs("my-sandbox", cmd.id, stream="stderr")

    # List all detached commands
    all_cmds = client.detached_list("my-sandbox")

    # Kill a running command
    client.detached_kill("my-sandbox", cmd.id)

Async version:

async with AsyncAgentKernel() as client:
    cmd = await client.exec_detached("my-sandbox", ["python3", "train.py"])
    logs = await client.detached_logs("my-sandbox", cmd.id)

Streaming

for event in client.run_stream(["python3", "script.py"]):
    if event.type == "output":
        print(event.data["data"], end="")

Configuration

client = AgentKernel(
    base_url="http://localhost:18888",  # default
    api_key="sk-...",                  # optional
    timeout=30.0,                      # default
)

Or use environment variables:

export AGENTKERNEL_BASE_URL=http://localhost:18888
export AGENTKERNEL_API_KEY=sk-...

License

MIT