Does Notebook Intelligence require JupyterLab?
Yes, Notebook Intelligence requires JupyterLab version 4.0.0 or higher.
@notebook-intelligence
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Notebook Intelligence (NBI) is an AI coding assistant and extensible AI framework for JupyterLab. It works with GitHub Copilot, any LLM provider, or local models via Ollama, boosting JupyterLab user productivity with in‑line code generation, auto‑complete, and a chat interface.
Install the extension with pip install notebook-intelligence (requires JupyterLab ≥ 4.0.0) and restart JupyterLab. Configure the LLM provider and model through the Settings dialog, via the command palette, or by editing the config file at ~/.jupyter/nbi-config.json.
Yes, Notebook Intelligence requires JupyterLab version 4.0.0 or higher.
Use the Notebook Intelligence Settings dialog (accessible from JupyterLab’s Settings menu or the `/settings` chat command) or manually edit `~/.jupyter/nbi-config.json`. API keys for custom providers are stored in that config file.
It supports both Standard Input/Output (stdio) and Server‑Sent Events (SSE) transports. MCP support is currently limited to server tools.
Notebook Intelligence uses the system keyring via the `keyring` package. To remember a token, launch JupyterLab with `--NotebookIntelligence.github_access_token=remember`; to forget it, use `forget`.
Yes. In the `mcp.tools` configuration of `nbi-config.json`, use the `"alwaysAllow"` key with a list of tool names to auto‑approve those specific tools.
Project context your AI can query through MCP — with source evidence.

Islamic source data for assistants: Quran, hadith, tafsir, duas, prayer times and qibla. Answers come from real sources with citations instead of recalled text, so verses and hadith are not hallucinated. 36,000+ hadiths

Exact, shared memory for AI agents: a hierarchical database where totals always add up, every write is attributed to the agent and person behind it, and any figure traces back to its source file. Agents create databases,

AI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them a memory that persists. Your agent's corrections, preference
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors
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