Notebook Intelligence
Notebook Intelligence (NBI) is an AI coding assistant and extensible AI framework for JupyterLab. It adds chat, inline edit, auto-complete, and an agent that can drive notebooks — backed by GitHub Copilot, an OpenAI-compatible or LiteLLM-compatible endpoint, local Ollama models, or Anthropic's Claude Code CLI.
NBI is free and open-source. Connect it to a free or paid LLM provider of your choice — GitHub Copilot, any OpenAI- or LiteLLM-compatible endpoint, Ollama (local), or Anthropic Claude (via the Claude Code CLI). Provider charges, when applicable, are paid directly to the provider.
Contents
- Requirements
- Quick start
- Concepts
- Feature highlights
- Configuration
- Built-in tools
- Model Context Protocol (MCP) support
- Rulesets
- Claude Skills
- Claude MCP Servers
- Claude Plugins
- Chat feedback
- Documentation
- Further reading
- Roadmap
- License
Requirements
- Python 3.10+
- JupyterLab 4.x
- Node.js — only required for Claude mode (the Claude Code CLI) and for MCP servers that launch via
npx. - A fresh virtualenv or conda env is recommended so NBI doesn't conflict with system Python.
Quick start
pip install notebook-intelligence
jupyter lab # restart JupyterLab if it was already running
After restart:
- Click the NBI icon in the left sidebar to open the chat panel.
- Open NBI Settings (gear icon in the chat panel, or Settings → Notebook Intelligence Settings).
- Sign into your provider — for GitHub Copilot, click Sign in; for an OpenAI- or LiteLLM-compatible endpoint, paste an API key; for Ollama, point at your local daemon. To use Claude, enable Claude mode (see below).
- Type a message in the chat panel and press Enter.
If the panel stays empty or login does nothing, see Troubleshooting.
Concepts
A short glossary you'll see referenced throughout these docs.
- LLM Provider — the service that runs the model. NBI ships with four provider adapters: GitHub Copilot, OpenAI-compatible, LiteLLM-compatible, and Ollama. Anthropic Claude is available through Claude mode, not as a top-level provider.
- Chat Participant — a
@mention-able persona inside the chat panel (@workspace,@mcp, …). Participants route the request to a specific tool surface. - Default mode vs Claude mode — Default uses the configured LLM Provider for chat, inline chat, and auto-complete. Claude mode uses the Claude Code CLI for the chat panel (gaining its tools, skills, MCP servers, and custom commands) and Claude models via the Anthropic API for inline chat and auto-complete. Requires the Claude Code CLI on
PATH. - Claude Code vs the Anthropic API — the Anthropic API (
api.anthropic.com) is the HTTPS endpoint NBI calls directly for inline chat and auto-complete in Claude mode. Claude Code is Anthropic's local CLI agent that NBI shells out to for the chat panel; it talks to Anthropic itself. - MCP — Model Context Protocol. A way for the LLM to call out to external tools (read files, hit APIs, run scripts).
- Ruleset — markdown files in
~/.jupyter/nbi/rules/that get injected into the system prompt to enforce conventions, coding standards, or domain rules. - Skill: a directory under
~/.claude/skills/(or<project>/.claude/skills/) holding aSKILL.mdplus helper files. Claude can invoke it like a callable plugin scoped to a workspace. - Claude plugin: a unit packaged for
claude plugin install, distributed through a marketplace (typically a GitHub repo that publishes a manifest of plugins). Distinct from NBI's own labextension; plugins run inside Claude Code sessions.
Feature highlights
Claude mode
NBI provides a dedicated mode for Claude Code integration. In Claude mode, NBI uses the Claude Code CLI for the chat panel, and Claude models (via the Anthropic API) for inline chat and auto-complete suggestions. This brings Claude Code's tools, skills, MCP servers, and custom commands into JupyterLab.
Configure via the NBI Settings dialog (gear icon in the chat panel, or Settings → Notebook Intelligence Settings). Toggle Enable Claude mode, then:
- Chat model — the Claude model used for the chat panel and inline chat.
- Auto-complete model — the Claude model used for auto-complete suggestions.
- Chat Agent setting sources — user, project, or both, mirroring Claude Code's settings.
- Chat Agent tools — which tool sets to activate. Claude Code tools are always on. Jupyter UI tools are NBI's own (authoring notebooks, running cells, etc.).
- API key and Base URL — point at Anthropic or a self-hosted endpoint.
If the Claude Code CLI is on PATH, NBI launches it automatically. To override the location, set the NBI_CLAUDE_CLI_PATH environment variable before starting JupyterLab.
Resuming a previous Claude session
When Claude mode is on, the chat sidebar shows a history icon next to the gear. Click it to list the Claude Code sessions recorded for the current working directory (the same transcripts the Claude Code CLI stores under ~/.claude/projects/). Selecting a session reconnects via resume, so the next message you send continues that transcript with full prior context.
Claude Code launcher tile
When the Claude CLI is on PATH, the JupyterLab launcher (the panel that opens with new tabs) shows a Claude Code tile alongside the standard kernel launchers. Clicking it opens a session picker; search across past transcripts and resume one in a fresh terminal, or start a new session in the file browser's active subdirectory. Session IDs are copyable from the picker for paste into a claude --resume <id> command.
Other coding-agent launcher tiles
When any of the following CLIs are on PATH, the launcher adds a tile for each. Clicking a tile opens a terminal at the file browser's current directory and runs the CLI:
- opencode (override path with
NBI_OPENCODE_CLI_PATH) - Pi (override path with
NBI_PI_CLI_PATH) - GitHub Copilot CLI (override path with
NBI_GITHUB_COPILOT_CLI_PATH) - OpenAI Codex (override path with
NBI_CODEX_CLI_PATH)
Tiles add and remove themselves as CLIs become available or unavailable; they do not require Claude mode.
Agent mode
In Agent mode, the built-in AI agent creates, edits, and executes notebooks for you interactively. It can detect issues in cells and fix them.
Code generation with inline chat
Use the sparkle icon on the cell toolbar or the keyboard shortcut to show the inline chat popover.
Ctrl+G / Cmd+G opens the popover. Ctrl+Enter / Cmd+Enter accepts the suggestion. Esc closes it. The accept shortcut overrides JupyterLab's default run cell binding only while the popover is open — outside the popover, Ctrl+Enter / Cmd+Enter still runs the active cell.
Auto-complete
Auto-complete suggestions are shown as you type. Tab accepts. NBI provides auto-complete in code cells and Python file editors.
Chat interface
You can paste or attach images alongside a chat prompt — the image goes to the model as input when the active model supports vision.
Cell output actions
Right-click a cell output (or hover for the toolbar) to send it straight into the chat as context:
- Explain cell errors — surfaces a "Troubleshoot errors in output" entry on cells that raised; opens a chat turn with the traceback attached.
- Ask about cell outputs — attaches the output as structured context for a follow-up question. Includes images for vision-capable models.
- Show output toolbar — the floating toolbar above each output with quick Explain / Ask / Troubleshoot actions.
Each is per-user toggleable from Settings (saved as enable_explain_error, enable_output_followup, enable_output_toolbar in config.json, default on) and admin-lockable via NBI_EXPLAIN_ERROR_POLICY / NBI_OUTPUT_FOLLOWUP_POLICY / NBI_OUTPUT_TOOLBAR_POLICY.
Notebook toolbar generation
Active notebooks show a sparkle icon on the toolbar. Click it to open a popover that scopes the generation request to that specific notebook — handy for multi-notebook sessions where you don't want the chat sidebar to compete for context.
Configuration
Configure your provider, model, and API key from NBI Settings — the gear icon in the chat panel, the /settings chat command, or the JupyterLab command palette. For background, see the provider blog post.