Open-source alternatives to NotebookLM

LightRAG

Graph-plus-vector RAG server with web UI and Ollama-compatible API

LightRAG indexes documents into a knowledge graph plus vector store and queries both layers, as a lighter alternative to Microsoft GraphRAG. The server package ships a REST API, a web UI for inserting and visualizing the graph, and Ollama-compatible /api routes for chat frontends. Parsing runs via MinerU, Docling or a native engine; production storage goes to PostgreSQL, Neo4j, MongoDB, Milvus or OpenSearch.

• no GPU
• Docker + Compose
• Needs PostgreSQL (recommended for production), Neo4j (optional), MongoDB (optional), Milvus (optional), OpenSearch (optional)
• Models: LLM and embedding providers configured in .env, tested with open models such as Qwen3-30B-A3B

Open Notebook

Self-hosted NotebookLM alternative with podcasts and 20+ model providers

Its own README calls it an alternative to NotebookLM.

Open Notebook collects PDFs, audio, video, web pages and Office files into notebooks and offers cited chat, full-text and vector search, notes and multi-speaker podcast generation. It runs as two containers (SurrealDB plus a FastAPI/Next.js app) and talks to OpenAI, Anthropic, Google, Mistral, Groq, Ollama, LM Studio or any OpenAI-compatible server. A REST API and MCP integration expose the same features.

• no GPU
• Docker + Compose
• Needs SurrealDB
• Models: OpenAI, Anthropic, Google, Mistral, Groq, DeepSeek, xAI, OpenRouter, Cohere, Ollama, LM Studio, oMLX and any OpenAI-compatible endpoint
• port 8502
• README: alternative to NotebookLM

RAGFlow

RAG engine with document parsing, agentic retrieval and citations

RAGFlow parses documents (Word, slides, Excel, TXT, images, scans, web pages) with template-based chunking, then retrieves with multiple recall and fused re-ranking to produce answers with traceable citations. Recent releases add agentic multi-step retrieval with four thinking modes and Knowledge Compilation into wikis, graphs, trees and mind maps. Models are configured by name, address and API key for the LLM, embedding and reranker.

• RAM ≥ 16 GB
• no GPU
• Docker + Compose
• Needs Elasticsearch or Infinity, MySQL, MinIO, NATS JetStream, Kvrocks, ClickHouse
• Models: configurable LLM, embedding, reranker
• port 80

WeKnora

Enterprise knowledge base combining RAG Q&A, agents and generated wikis

WeKnora turns team documents into knowledge bases with three modes: cited RAG answers, an agent that runs multi-step tasks with skills in Docker, E2B or Cube sandboxes, and auto-generated wiki pages with a knowledge graph. It syncs from Feishu, Confluence, GitLab, Notion and RSS, answers in WeCom, Slack and Telegram, and exposes an MCP server. Deploy with Docker Compose, Helm or one Lite binary on SQLite.

• no GPU
• Docker + Compose
• Needs Neo4j (optional profile), MinIO (optional profile), Langfuse (optional profile)
• Models: OpenAI, DeepSeek, Qwen, Zhipu, Hunyuan, Gemini, MiniMax, NVIDIA, LiteLLM, Ollama
• port 80

SurfSense

Offline NotebookLM alternative that turns documents into decks, reports and podcasts

Its own README calls it an alternative to NotebookLM.

SurfSense indexes local PDFs, Office files and images into SQLite, answers with citations and turns sources into summaries, flashcards, quizzes, mind maps, editable pptx/docx/xlsx and offline podcasts (Kokoro-82M). It runs a local Qwen3 (six sizes from 0.5 GB) or any OpenAI-compatible API, egress off by default. The supported path is a desktop installer; the Docker stack is community-supported.

• no GPU
• Docker + Compose
• Models: local Qwen3 in six sizes from 0.5 GB, any OpenAI-compatible API
• README: alternative to NotebookLM

MaxKB

Enterprise knowledge-base agent platform with RAG, workflows and MCP tools

MaxKB runs as one Docker container (port 8080, state in one volume) with a RAG pipeline that uploads or crawls documents, a workflow engine with function library and MCP tool use, and zero-code embedding into other systems. It works with private models (DeepSeek, Llama, Qwen) and public APIs (OpenAI, Claude, Gemini, MiniMax) and handles text, image, audio and video. Built on Django, LangChain and PostgreSQL.

• no GPU
• Models: OpenAI, Claude, Gemini, MiniMax, DeepSeek, Llama, Qwen as private models
• port 8080

PrivateGPT

Anthropic-style API layer for private RAG on local inference servers

PrivateGPT 1.0 is an API server shaped like the Anthropic Messages API, adding file ingestion, retrieval with citations, web search, code execution, MCP and direct database or CSV querying. It runs no models; it calls any OpenAI-compatible server (Ollama, llama.cpp, vLLM) through OPENAI_API_BASE. A workbench UI at /ui on port 8080 exists for testing; the API is the product.

• no GPU
• Docker
• Needs OpenAI-compatible inference server (Ollama, llama.cpp, vLLM)
• Models: any model behind an OpenAI-compatible /v1/chat/completions endpoint
• port 8080

PipesHub

Permission-aware search and agent context over 50+ workplace systems

PipesHub indexes Slack, Google Drive, GitHub, Microsoft 365, Notion and 50+ systems into a knowledge graph (Neo4j or ArangoDB), Qdrant and MongoDB, then serves permission-aware search with block-level citations and hands the same context to agents over MCP and SDKs. Access is checked against source permissions at query time. A one-command installer writes Compose files and starts the stack on port 3000.

• no GPU
• Docker + Compose
• Needs Neo4j or ArangoDB, Qdrant, MongoDB, Redis, Kafka (larger deployments)
• Models: any LLM provider, bring your own model, Ollama, local embedding server by default
• port 3000

DeepWiki-Open

Generates browsable wikis and diagrams for GitHub, GitLab and Bitbucket repos

DeepWiki-Open takes a repository URL from GitHub, GitLab or Bitbucket, analyzes the code structure, generates documentation and diagrams, organizes them into a navigable wiki and builds a codemap for guided tours. The repo ships a Dockerfile and compose file. The README now points to a 2.0 release called Grok Wiki distributed as a download from grok-wiki.com and no longer documents configuration.

• no GPU
• Docker + Compose

DB-GPT

Agentic data assistant that writes SQL and code over your databases

DB-GPT connects to databases, CSV and Excel files, warehouses and knowledge bases, then plans tasks, writes SQL and Python, runs them in sandboxes and produces charts, dashboards and HTML reports. It installs with pip install dbgpt-app (Python 3.10+) plus a setup wizard and serves a web UI on port 5670, with OpenAI-compatible, DashScope, Moonshot and MiniMax profiles and local models via vLLM or llama.cpp.

• GPU optional
• Docker + Compose
• Compose runs MySQL
• Models: OpenAI-compatible APIs, DashScope/Tongyi, Moonshot (Kimi), MiniMax, local models via vLLM or llama.cpp: DeepSeek, Qwen, GLM, Llama, Gemma, Yi
• port 5670