swarmauri_tool_jupyterstartkernel 0.9.3.dev16

A tool designed to start a new Jupyter kernel programmatically using jupyter_client, enabling execution of notebook cells.

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Swarmauri Tool · Jupyter Start Kernel

A Swarmauri orchestration tool that spins up Jupyter kernels on demand using jupyter_client. The helper wraps connection-file management, kernel specification, and timeout handling so automation pipelines, notebook CI, or Swarmauri agents can acquire fresh kernels with one function call.

• Launches kernels with configurable names and kernel-spec overrides.
• Surfaces ready-to-use connection metadata for downstream orchestration.
• Keeps a reference to the underlying KernelManager so you can interact with the kernel lifecycle after launch.

Requirements

• Python 3.10 – 3.13.
• The environment must have Jupyter kernel specs installed (for example the default python3).
• Dependencies (jupyter_client, swarmauri_base, swarmauri_standard, pydantic) install automatically.

Installation

Install via the packaging tool that matches your workflow. Each command fetches transitive dependencies.

pip

pip install swarmauri_tool_jupyterstartkernel

Poetry

poetry add swarmauri_tool_jupyterstartkernel

uv

# Add to the current project and update uv.lock
uv add swarmauri_tool_jupyterstartkernel

# or install into the active environment without touching pyproject.toml
uv pip install swarmauri_tool_jupyterstartkernel

Tip: When using uv inside this repository, run commands from the repository root so uv can resolve the shared pyproject.toml.

Quick Start

The tool behaves like a callable. Instantiate it and optionally pass a kernel_name, timeout, or kernel spec.

from swarmauri_tool_jupyterstartkernel import JupyterStartKernelTool

start_kernel = JupyterStartKernelTool()
result = start_kernel()  # defaults to python3

print(result)
# {
#   'status': 'success',
#   'kernel_id': '03c7d8f9-ec4d-4a8a-8a90-cdb35ff9e6c9',
#   'kernel_name': 'python3',
#   'connection_file': '/Users/.../jupyter/runtime/kernel-03c7d8f9.json'
# }

A non-success status signals the kernel failed to spawn (missing kernelspec, permission issue, etc.).

Usage Scenarios

Launch With Custom Specification

from swarmauri_tool_jupyterstartkernel import JupyterStartKernelTool

start_kernel = JupyterStartKernelTool()
config = {
    "env": {"EXPERIMENT_FLAG": "1"},
    "resource_limits": {"memory": "1G"}
}

custom = start_kernel(kernel_name="python3", kernel_spec=config, startup_timeout=20)

if custom["status"] == "success":
    print(f"Kernel ready at {custom['connection_file']}")
else:
    raise RuntimeError(custom["message"])

Pass a kernel_spec dict to tweak environment variables or other launch parameters that the underlying KernelManager accepts.

Pair With the Shutdown Tool in an Automated Flow

from swarmauri_tool_jupyterstartkernel import JupyterStartKernelTool
from swarmauri_tool_jupytershutdownkernel import JupyterShutdownKernelTool

start_kernel = JupyterStartKernelTool()
shutdown_kernel = JupyterShutdownKernelTool()

launch = start_kernel(kernel_name="python3")
if launch["status"] != "success":
    raise RuntimeError(launch["message"])

kernel_id = launch["kernel_id"]
print(f"Kernel started: {kernel_id}")

# ... run your notebook execution workflow ...

cleanup = shutdown_kernel(kernel_id=kernel_id, shutdown_timeout=10)
print(cleanup)

Use this pairing in CI pipelines or agent flows that must guarantee kernels are torn down after execution.

Integrate Inside a Swarmauri Agent

from swarmauri_core.agent.Agent import Agent
from swarmauri_core.messages.HumanMessage import HumanMessage
from swarmauri_standard.tools.registry import ToolRegistry
from swarmauri_tool_jupyterstartkernel import JupyterStartKernelTool

registry = ToolRegistry()
registry.register(JupyterStartKernelTool())

agent = Agent(tool_registry=registry)
message = HumanMessage(content="start a python3 kernel for my notebook batch job")
response = agent.run(message)
print(response)

The agent resolves the registered tool, starts a kernel, and returns the connection metadata to the conversation context.

Troubleshooting

• No such kernel – The requested kernel_name is not installed. Check jupyter kernelspec list.
• Kernel start timeout exceeded – Increase startup_timeout for slow environments or pre-warm interpreters.
• Permission errors – Ensure the process can create files inside Jupyter's runtime directory (usually ~/.local/share/jupyter/runtime).

License

swarmauri_tool_jupyterstartkernel is released under the Apache 2.0 License. See LICENSE for details.