Open-source SciSpace alternatives

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.

GPT Researcher is an open-source autonomous agent that performs deep research on a given task and writes up a report with citations. It can work from web sources and from local documents, and it works with any LLM provider, aiming for factual, unbiased output rather than a quick chatbot answer.

The project positions itself against several limits of manual and LLM-only research: manual work can take weeks, models trained on old data may hallucinate, token limits make long reports hard, and a narrow source set leads to shallow or wrong conclusions. Drawing on Plan-and-Solve and RAG ideas, it splits research into planning and parallelized agent work to increase speed and keep behavior stable. Customization options let you tailor domain-specific research agents, and topics note MCP server support.

GPT Researcher is written in Python and Apache-2.0 licensed, with documentation in several languages and a website at gptr.dev. You run it yourself with your own model API keys, and it suits analysts, students and developers who want automated, source-backed reports on a topic.

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.

Scira, formerly called MiniPerplx, is a minimalistic AI search tool that finds information online and cites where it came from. The project describes it as an agentic research platform that plans, retrieves and cites, and it is available as a hosted site at scira.ai.

You can start by asking a question, uploading a PDF or pasting a link. Behind it, the README credits the Vercel AI SDK for model integration and streaming, Exa AI for web search and content retrieval, and Upstash for serverless Redis and rate limiting. It is written in TypeScript as a web app you can run yourself with your own provider keys.

Scira is licensed under AGPL-3.0, so modified versions offered over a network must share their source. It suits developers who want a reference AI search app to run or adapt, and individuals who want a cited, minimalistic alternative to conventional search.