Lightweight JupyterLab with real-time collaboration on port 8888. No GPU required. Based on fedora (not nvidia), supports both amd64 and arm64. MUST be invoked before building, deploying, configuring, or troubleshooting the jupyter image.
Lightweight JupyterLab with real-time collaboration via jupyter-collaboration (Y-CRDT).
| Property | Value |
|---|---|
| Base | fedora |
| Layers | agent-forwarding, jupyter (sub-layers: jupyter-mcp), notebook-templates, dbus, ov |
| Platforms | linux/amd64, linux/arm64 |
| Ports | 8888 |
| Registry | ghcr.io/overthinkos |
fedora (Fedora 43 base — no GPU)pixi → python → supervisord (transitive)jupyter — JupyterLab + jupyter-collaboration + data science (composes jupyter-mcp sub-layer for MCP extension)notebook-templates — Starter notebooks (data layer, seeds ~/workspace)agent-forwarding — SSH/GPG agent forwardingdbus — D-Bus session busov — ov CLI binary| Port | Service | Protocol |
|---|---|---|
| 8888 | JupyterLab | HTTP |
| Name | Path | Purpose |
|---|---|---|
| workspace | ~/workspace | Persistent notebook storage |
ov image build jupyter
ov config jupyter
ov start jupyter
# Open http://localhost:8888
/ov-layers:jupyter — JupyterLab + collaboration + data science/ov-layers:agent-forwarding — SSH/GPG forwarding/ov-images:jupyter-ml — GPU-accelerated variant (nvidia base, full ML stack)/ov-images:jupyter-ml-notebook — GPU variant with fine-tuning notebooks/ov-images:openwebui — Open WebUI consumes jupyter for code execution and MCP tools/ov-images:hermes — Hermes agent consumes jupyter MCP for notebook manipulation/ov-images:fedora — parent base imageThe image includes a built-in MCP server at http://localhost:8888/mcp (Streamable HTTP transport, MCP spec 2025-11-25). AI agents can create, read, edit, execute, and watch notebooks programmatically — with changes syncing live to all collaborators via CRDT.
The MCP server name is set via the MCP_SERVER_NAME environment variable (default: jupyter). For multi-instance deployments, override per-instance: ov config jupyter -i work -e MCP_SERVER_NAME=jupyter-work.
The ov-jupyter plugin in plugins/ov-jupyter/ declares the MCP server. To connect manually:
claude mcp add --transport http --scope project jupyter http://localhost:8888/mcp
This creates .mcp.json at the project root:
{"mcpServers": {"jupyter": {"type": "http", "url": "http://localhost:8888/mcp"}}}
| Category | Tools |
|---|---|
| Notebook management | list_notebooks, get_notebook, create_notebook |
| Cell operations (CRDT) | get_cell, update_cell, insert_cell, delete_cell, execute_cell |
| Room management | open_notebook_session, close_notebook_session |
| Change watching | watch_notebook (blocks until change or timeout) |
| Collaboration | get_active_users, get_active_sessions |
See /ov-layers:jupyter for full parameter and return type documentation.
claude -p# Prerequisites: ov start jupyter
# Create and work with a notebook
claude -p "Call create_notebook with path 'test.ipynb'"
claude -p "Call open_notebook_session with path 'test.ipynb'"
claude -p "Call insert_cell with path 'test.ipynb', index 0, source 'print(42)', cell_type 'code'"
claude -p "Call execute_cell with path 'test.ipynb' and index 0"
claude -p "Call close_notebook_session with path 'test.ipynb'"
Multiple claude -p sessions (or any MCP client) can edit the same notebook simultaneously. Each session creates a separate HTTP connection but shares the same CRDT document. Changes from one client are immediately visible to all others.
# Client A watches for changes
claude -p "Call watch_notebook with path 'test.ipynb' and timeout 30" &
# Client B makes a change
claude -p "Call update_cell with path 'test.ipynb', index 0, source 'print(\"hello\")'"
# Client A's watch_notebook returns: {"changed": true, "cell_count": 1}
After ov start:
# Container and services running
ov status jupyter
ov service status jupyter
# JupyterLab responds
curl -s -o /dev/null -w '%{http_code}' http://localhost:8888 # 200
# MCP server responds
curl -s http://localhost:8888/mcp -X POST \
-H 'Content-Type: application/json' \
-H 'Accept: application/json, text/event-stream' \
-d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-11-25","capabilities":{},"clientInfo":{"name":"test","version":"0.1.0"}}}'
# Expected: SSE response with serverInfo.name = "jupyter"
# Server extensions
ov shell jupyter -c "jupyter server extension list 2>&1 | grep -E 'ydoc|colab_mcp'"
# Expected: jupyter_server_ydoc enabled OK, jupyter_mcp 0.1.0 OK
# MCP tools via Claude Code
claude mcp list # Should show: jupyter: http://localhost:8888/mcp (HTTP) - ✓ Connected
To test real-time collaboration, deploy sway-browser-vnc alongside:
ov start sway-browser-vnc
# Open JupyterLab in two Chrome tabs via container DNS:
ov cdp open sway-browser-vnc "http://ov-jupyter:8888/lab"
# Open second tab
ov cdp open sway-browser-vnc "http://ov-jupyter:8888/lab"
Executing cells via CDP: Use Input.dispatchKeyEvent (not VNC keys — unreliable when Chrome lacks compositor focus):
TAB=<tab-id>
# Focus cell, then Shift+Enter via CDP
ov cdp eval sway-browser-vnc $TAB "document.querySelector('.jp-Cell-inputArea .cm-content')?.focus()"
ov cdp raw sway-browser-vnc $TAB 'Input.dispatchKeyEvent' '{"type":"rawKeyDown","windowsVirtualKeyCode":13,"nativeVirtualKeyCode":13,"modifiers":8}'
ov cdp raw sway-browser-vnc $TAB 'Input.dispatchKeyEvent' '{"type":"keyUp","windowsVirtualKeyCode":13,"nativeVirtualKeyCode":13,"modifiers":8}'
MUST be invoked when the task involves the jupyter image, collaborative Jupyter notebooks, lightweight Jupyter deployments without GPU, MCP-based notebook access, or multi-client collaboration. Invoke this skill BEFORE reading source code or launching Explore agents.