Open-source NotebookLM alternatives

AnythingLLM is an all-in-one AI application for chatting with your own documents and automating work with AI agents. It runs locally by default, lets you connect local or cloud language models, and is written in JavaScript under the MIT license. The README describes it as a way to build a private, fully featured assistant in the style of ChatGPT.

Vector databases, document processing pipelines, multi-user support and agents all come included. Newer features listed include dynamic model routing based on rules you define, automatic and user-managed memories, scheduled tasks with agent capabilities, skill selection, a no-code agent builder, MCP compatibility and multi-modal support for both closed and open-source models.

It is available as a desktop app for Mac, Windows and Linux, as a Docker version that adds multi-user access control, and as a hosted instance, and there is an open-source Android app. Administrators can control access and experience per user. It suits individuals and teams who want a private document-chat and agent workspace without much setup.

Open Notebook is a privacy-oriented, open-source option for people who want something like Google's NotebookLM for researching and learning with AI. It is written in TypeScript and released under the MIT license. The project describes itself as private, multi-model and fully local, so your research material stays on infrastructure you control instead of a single cloud provider.

It supports more than a dozen AI providers, including OpenAI, Anthropic, Ollama and LM Studio, and lets you organize multimodal content such as PDFs, videos, audio and web pages. Features include podcasts with several speakers, keyword and vector search over everything you add, and chat conversations grounded in your own sources. The interface is available in several languages, including Portuguese, Chinese, Japanese, Russian and Bengali.

The README compares Open Notebook with Google NotebookLM across privacy and control, choice of AI provider and more, and links a getting-started guide, user guide, feature list and deployment instructions. A Discord server is open for help and workflow ideas. It suits students, researchers and knowledge workers who want a NotebookLM-style workflow with their own choice of models.

SurfSense is a free, open-source desktop app positioned as an air-gapped alternative to NotebookLM. You drop in documents you already have, ask questions and receive answers with citations to the sources, then convert the same material into a briefing, slide deck, report, study guide or podcast. Everything runs on your own machine, which makes it suitable for documents you cannot upload to a cloud service.

The search index lives on your disk, you choose the model, and the app uploads nothing. You may supply your own model API key, or have the app download a local model, and no account is required. Installers are provided for Windows x64, macOS on Apple Silicon, and Linux as an AppImage or deb package. The project also links documentation, a comparison with other tools, pricing information and a Discord community, and topics mention RAG, agents and web scraping.

SurfSense is written in Python, and its license is listed as 'Other' on GitHub, so confirm the terms. It suits researchers, analysts and professionals handling confidential material who want NotebookLM-style features while keeping data local.

Khoj is a personal AI app that you can run on your own machine or scale up to an enterprise cloud deployment. You can chat with local or online models, such as Llama, Qwen, Gemma, Mistral, GPT, Claude, Gemini and DeepSeek, and get answers drawn from the internet and from your own documents, including images, PDFs, Markdown, org-mode files, Word documents and Notion pages.

It can be reached from a browser, Obsidian, Emacs, a desktop app, a phone or WhatsApp. Users create agents with their own knowledge, persona, chat model and tools, set up automations that deliver personal newsletters and smart notifications by email, and use advanced semantic search to find relevant documents. It can also generate images and speak responses aloud. The README highlights benchmark results for retrieval and reasoning and introduces Pipali, a separate open-source AI coworker for your own computer.

Khoj is written in Python and licensed under AGPL-3.0. You can self-host it, with offline models via llama.cpp, or use the hosted app. It suits individuals and teams who want an AI assistant grounded in their notes and files while keeping a choice of model.

Podcastfy is an open-source Python package that transforms multimodal content into engaging audio conversations using generative AI. Inputs can include websites, PDFs, images, YouTube videos and user-provided topics, and the output is a podcast-style dialogue in many languages.

It is presented as an open-source, programmatic alternative to the podcast feature of NotebookLM. Unlike closed UI-based tools centered on research synthesis, Podcastfy focuses on customization and scale, so developers can generate audio through a Python API, a command-line interface or a web app. Topics reference providers such as ElevenLabs, Gemini and OpenAI for text-to-speech and language models, and the project has an accompanying paper.

Podcastfy is licensed under Apache-2.0 and you run it yourself with your own API keys for the AI services it calls. It suits developers, educators and content creators who want to automate audio summaries or learning material from documents and web pages.

Twocast is a tool for generating two-person podcast episodes automatically from a piece of content, aimed at creators and researchers who want a quick audio summary without recording anything themselves. It is comparable to Google's NotebookLM podcast feature, but focused specifically on multi-language, two-voice output.

It can generate a 3-to-5-minute podcast with one click from a topic, a link, a document (doc, PDF or text file), or a longer list page that produces a 5-to-9-minute episode, with multi-language support and downloadable audio. Each generated podcast includes the audio itself along with an outline and full script, and it integrates with three text-to-speech and generation backends: Fish Audio, Minimax and Google Gemini, alongside an LLM API for chat and search functionality through providers such as OpenRouter and x.ai.

Twocast can be run locally by configuring environment variables and a PostgreSQL database, or started with a single Docker command. It is written in TypeScript, and the repository metadata does not state a license, so check the repository before relying on it for commercial use; it requires API keys from the chosen TTS and LLM providers, which are billed separately by those providers.