jupyter-jcli 0.3.0

CLI tool for LLM agents to operate Jupyter Lab servers

jupyter-jcli

CLI tool for LLM agents to operate Jupyter Lab servers.

j-cli enables AI agents (and humans) to remotely control Jupyter servers — execute code in kernels, manage sessions, and write outputs back to notebooks, all from the command line.

Installation

# latest release
uv tool install jupyter-jcli

# latest dev version
uv tool install git+https://github.com/tttpob/jcli.git

Requires Python 3.10+.

Recommended Workflow

1. Set up environment variables

Use direnv so the env vars are loaded automatically whenever you enter the project directory:

# .envrc
export JCLI_JUPYTER_SERVER_URL=http://localhost:8888
export JCLI_JUPYTER_SERVER_TOKEN=your-token

direnv allow

2. Launch Jupyter

# stdout is pipe-safe — the hint line goes to stderr
$(j-cli serve-cmd --serve-backend lab)

This prints (and immediately executes) a command like:

jupyter lab --ServerApp.token="$JCLI_JUPYTER_SERVER_TOKEN" \
    --ServerApp.ip=localhost --ServerApp.port=8888 --no-browser

The token value is never inlined; it is always referenced as $JCLI_JUPYTER_SERVER_TOKEN.

3. Verify connectivity

j-cli healthcheck

4. Set up hooks (once per project)

Install Claude Code hooks so the AI redirects notebook edits through j-cli:

j-cli setup claude

Install the git pre-commit hook to keep .py / .ipynb pairs in sync:

j-cli setup git

If your notebooks live in a subdirectory, limit pair detection to that path (avoids false positives elsewhere in the repo). --include can be repeated:

j-cli setup git --include "notebooks/*"
# or multiple directories
j-cli setup git --include "notebooks/*" --include "experiments/*"

Commands

Global Options

Flag	Description
-s, --server-url	Jupyter server URL (env: JCLI_JUPYTER_SERVER_URL, default: http://localhost:8888)
-t, --token	Auth token (env: JCLI_JUPYTER_SERVER_TOKEN)
-j, --json	Output as JSON for programmatic use
--version	Show version

healthcheck

Check server connectivity and running kernel count.

j-cli healthcheck

kernelspec list

List available kernel specifications.

j-cli kernelspec list

session

j-cli session create --kernel python3 --name my-session
j-cli session list
j-cli session kill <session_id>

kernel

j-cli kernel interrupt <session_id>
j-cli kernel restart <session_id>

setup claude

Install Claude Code hooks (PreToolUse and PostToolUse) that intercept notebook-execution bypass tools and keep .py / .ipynb pairs in sync, redirecting Claude to use j-cli instead.

j-cli setup claude           # default: .claude/settings.local.json (gitignored)
j-cli setup claude --project # .claude/settings.json (committed, team-shared)
j-cli setup claude --user    # ~/.claude/settings.json (global, all projects)

# remove all j-cli managed hooks from the target file
j-cli setup claude --remove
j-cli setup claude --project --remove

The install command is idempotent — re-running updates hooks in place without duplicating them. --remove prunes only j-cli managed entries, preserving any unrelated user hooks. If the settings file becomes empty after removal it is deleted.

setup git

Install a pre-commit hook shim that runs j-cli _hooks pre-commit-pair-sync and update .gitignore to exclude paired .ipynb files.

j-cli setup git              # default: .githooks/pre-commit + set core.hooksPath
j-cli setup git --local      # .git/hooks/pre-commit (this clone only)
j-cli setup git --include "src/*.py"  # only sync matching files

# remove the managed hook and gitignore block
j-cli setup git --remove
j-cli setup git --local --remove

--remove deletes the hook only if it was written by j-cli, leaves core.hooksPath alone if it points to a non-j-cli directory, and removes the managed .gitignore block. Unrecognised hooks are skipped with a warning.

setup codex (not yet available)

Codex hook support is blocked upstream: the Codex hook API currently only reports the tool name bash, which means we cannot match NotebookEdit or Edit|Write the way we do for Claude Code. Once Codex exposes per-tool names we will add j-cli setup codex. Track upstream progress at https://developers.openai.com/codex/hooks.

serve-cmd

Print a copy-pasteable Jupyter launch command that references the token via an environment variable rather than inlining it.

# set env vars (token is never echoed to the terminal)
export JCLI_JUPYTER_SERVER_URL=http://localhost:8888
export JCLI_JUPYTER_SERVER_TOKEN=your-token

j-cli serve-cmd --serve-backend lab
# → jupyter lab --ServerApp.token="$JCLI_JUPYTER_SERVER_TOKEN" \
#       --ServerApp.ip=localhost --ServerApp.port=8888 --no-browser

# override host / port / root dir
j-cli serve-cmd --serve-backend lab --ip 0.0.0.0 --port 9000 --root-dir /work

# remove --no-browser (useful for desktop Jupyter)
j-cli serve-cmd --serve-backend notebook --browser

# JSON output (for programmatic use)
j-cli -j serve-cmd --serve-backend server

The hint line (# paste this into a shell …) is written to stderr so the command itself can be used safely in $() substitution. The token reference "$JCLI_JUPYTER_SERVER_TOKEN" is always a literal shell variable reference — the actual token value is never inlined.

--serve-backend must be one of lab, server, or notebook.

vars

Inspect variables in a kernel session.

# list all variables (NAME / TYPE / VALUE table)
j-cli vars <session_id>

# inspect a single variable
j-cli vars <session_id> --name x

# rich inspection (MIME-typed data, DAP kernels only)
j-cli vars <session_id> --name x --rich

# JSON output for programmatic use
j-cli -j vars <session_id>
j-cli -j vars <session_id> --name x

Source: when the kernel advertises debugger support (kernel_info_reply.supported_features contains "debugger"), the DAP inspectVariables control-channel path is used (source="dap"). Otherwise a shell-channel code snippet is executed (source="fallback").

Ordering caveat: variables are returned in first-definition order (CPython dict insertion order). Re-assigning a variable does not move it to the end; only del x; x = … does. Do not infer recency from position in the list.

No mtime: the Jupyter debug protocol does not expose per-variable last-modified timestamps. No mtime or last_execution_count field is available in the protocol.

session list variable preview

By default, session list fetches a short variable preview for each idle kernel:

j-cli session list            # includes VARS column (default)
j-cli session list --no-vars  # faster, skips variable fetch
j-cli session list --vars     # force fetch even when >10 sessions

Each session row gets a VARS column showing the first 5 variable names. A hint line at the bottom points at j-cli vars <SESSION_ID> for the full list.

In JSON mode (-j), each session object gains a vars_preview key:

{"session_id": "...", "vars_preview": {"names": ["x", "df"], "total": 2}}

exec

Execute code in a kernel session. Supports inline code, py:percent files, and Jupyter notebooks.

# inline code
j-cli exec <session_id> --code "import pandas as pd; df = pd.read_csv('data.csv'); df.head()"

# execute from py:percent file
j-cli exec <session_id> --file analysis.py

# execute specific cells from a notebook
j-cli exec <session_id> --file notebook.ipynb --cell 0:3

# execute a single cell
j-cli exec <session_id> --file notebook.ipynb --cell 5

Cell spec formats (0-indexed):

Spec	Meaning
3	Cell 3 only
3:7	Cells 3, 4, 5, 6
3:	Cell 3 to end
:5	Cells 0 through 4

Notebook writeback: When executing from a py:percent file (one with # %% cell markers or a # --- front matter block), outputs are automatically written back to the paired .ipynb. If analysis.ipynb does not yet exist, j-cli creates it automatically. Plain Python scripts without markers are executed normally without creating a notebook.

Py:Percent Format

j-cli supports the py:percent format — plain Python files with cell markers:

# ---
# jupyter:
#   kernelspec:
#     name: python3
# ---

# %%
import numpy as np

# %%
x = np.random.randn(100)
print(x.mean())

Development

# install with test dependencies
uv sync --extra test

# run tests (requires a real Jupyter server, started automatically by fixtures)
uv run pytest -v

License

MIT