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libro

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Basic Information

Libro is a modular, web‑first notebook product designed to provide a full notebook workbench with easy integration and kernel‑level extensibility. It is distributed as a Python package installable via pip and launched via a simple CLI command that starts a web server and opens the notebook in a browser. The project targets developers, data practitioners and teams who want an extensible notebook environment with built‑in AI workflows and model interaction. The README emphasizes quick start instructions, supported Python versions (3.10–3.12), an architectural overview, and planned improvements such as browser‑side execution and version diff integration. The repo bundles UI features like Prompt Cells and Sql Cells and integration points for LangChain and custom model extensions so users can connect models and databases within notebook workflows.

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App URL
https://github.com/weavefox/libro
Github Repository
https://github.com/weavefox/libro/blob/main/README.md

App Details

Features
Libro offers a complete notebook capability set plus AI‚Äëcentric extensions. Key features documented include Prompt Cell for direct large model interaction with multi‚Äëmodal support, chat contexts and the ability to save prompt outputs as langchain AIMessage variables. Sql Cell enables interactive SQL execution against connected databases inside the notebook. Built‚Äëin AI assistants provide code completion, error fixing, conversational cell chat, code explanation and optimization. The project exposes kernel‚Äëlevel extension points so every layer can be customized or extended. It includes built‚Äëin OpenAI model support and explicit extension paths via LangChain and libro‚Äëai. Distribution and operation are simple: pip install libro and run the libro command to launch the web UI. The README also shows architecture diagrams and a roadmap of planned AI integrations.
Use Cases
Libro helps developers and data users accelerate interactive development and AI‚Äëaugmented workflows by combining notebook interactivity with model integrations and database connectivity. Its AI features can speed up coding tasks through completion, automated error repair, explanation and optimization within cells. Prompt Cells let users carry conversational contexts and reuse model outputs programmatically, which simplifies experimentation with large models. Sql Cells let analysts run and inspect database queries inline, reducing context switching. Kernel‚Äëlevel extensibility and LangChain compatibility let teams adapt the platform to internal models and pipelines. The one‚Äëcommand launch and pip packaging lower onboarding friction. Roadmap items such as client‚Äëside execution and version diff aim to improve collaboration and reproducibility.

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