Best Self Hosted Alternatives to Google NotebookLM

Open WebUI is a web-based, extensible AI interface that provides a unified GUI for interacting with local and cloud LLMs. It supports multiple LLM runners and OpenAI-compatible APIs, built-in RAG, artifact storage, and collaboration features.

Key Features

• Multi-runner support (Ollama and OpenAI-compatible endpoints) and built-in inference integrations for flexible model selection
• Local Retrieval-Augmented Generation (RAG) with support for multiple vector databases and content extractors
• Image generation and editing integrations with local and remote engines; prompt-based editing workflows
• Granular role-based access control (RBAC), user groups, and enterprise provisioning (SCIM, LDAP/AD, SSO integrations)
• Persistent artifact/key-value storage for journals, leaderboards, and shared session data
• Progressive Web App (PWA) experience, responsive UI, and multi-device support
• Native Python function-calling tools (BYOF) and a web-based code editor for tool/workspace development
• Docker/Kubernetes deployment options, prebuilt image tags for CPU/GPU and Ollama bundles
• Production observability with OpenTelemetry traces, metrics and Redis-backed session management

Use Cases

• Teams wanting a central, auditable chat interface to query multiple LLMs and manage permissions
• Knowledge workers and developers using local RAG pipelines to query private document collections securely
• Experimentation and model comparison workflows combining multiple models, image tools, and custom functions

Limitations and Considerations

• Advanced features (model inference, heavy image generation) require external runners or GPU resources; performance depends on the chosen backend
• Some enterprise integrations and optional storage backends require additional configuration and credentials
• Desktop app is experimental; recommended production deployment paths are Docker, Docker Compose or Kubernetes

Open WebUI is positioned as a flexible interface layer for LLM workflows, emphasizing provider-agnostic integration, RAG, and enterprise features. It is suited for teams that need a full-featured, customizable web UI for local and cloud model workflows.

AnythingLLM is a full-stack AI application for building a private ChatGPT-like experience around your own documents and content. It supports local and hosted LLMs, integrates with multiple vector database backends, and organizes content into isolated workspaces for cleaner context management.

Key Features

• Retrieval-augmented generation (RAG) to chat with PDFs, DOCX, TXT, CSV, codebases, and more
• Workspace-based organization with separated context and optional document sharing
• AI agents, including a no-code agent builder and MCP compatibility
• Supports local and commercial LLM providers (including Ollama and llama.cpp-compatible models)
• Multiple vector database options (default local-first setup, with external backends available)
• Multi-user deployment with permissions (Docker deployment)
• Embeddable website chat widget (Docker deployment)
• Developer API for integrations and automation

Use Cases

• Internal knowledge base chat for teams (policies, runbooks, product docs)
• Private document Q&A for sensitive datasets and client files
• Building agent-assisted workflows that reference curated business content

AnythingLLM is a strong choice when you want a configurable, privacy-conscious AI application that can run locally or on a server, while staying flexible about which LLM and vector database you use.

Khoj is an open-source personal AI platform that combines chat, semantic document search, custom agents and scheduled automations. It can run locally or as a cloud-hosted service and integrates with local or remote LLMs to answer questions, generate content and automate research.

Key Features

• Multi-client access: web, desktop, Obsidian, Emacs, mobile (PWA) and chat integrations (e.g., WhatsApp).
• Model-agnostic LLM support: connect local GGUF models or remote OpenAI-compatible, Anthropic and Google-compatible endpoints; supports on-device and cloud models.
• Semantic search and embeddings: document ingestion (PDF, Markdown, Word, org-mode, Notion, images) with vector storage and retrieval for fast, contextual search.
• Custom agents and automations: build agents with distinct personas, tools and knowledge bases; schedule research tasks and email newsletters.
• Document processing and code tools: built-in extractors, simple code execution sandbox support (local Terrarium or remote sandboxes) and image generation features.
• Enterprise & self-hosting options: deploy via Docker or pip, use Postgres with pgvector for embeddings, and configure authentication and domains.

Use Cases

• Personal knowledge management: query a private document corpus and get grounded answers across notes, PDFs and files.
• Research automation: schedule recurring research queries and receive summarized results by email.
• Team/private deployments: host a private assistant for a team with custom agents, model selection and on-premise data control.

Limitations and Considerations

• Some optional integrations require extra setup or external services (e.g., code sandboxes, email providers); self-hosting needs correct environment configuration.
• A few plugins/integrations may be unmaintained or platform-specific; users should check the chosen connectors and follow the docs for compatibility and maintenance status.

Khoj is designed to be extensible and model-agnostic, emphasizing private data control and flexible deployment. It is suited for individuals and teams who need a searchable, automatable assistant that can run with local or cloud language models.

Perplexica is a privacy-focused AI answering engine designed to run on your own hardware. It combines web search results with local or hosted LLMs to generate natural-language answers with cited sources.

Key Features

• Web search integration powered by SearxNG to aggregate results from multiple engines
• Supports local models via Ollama and multiple cloud LLM providers via API configuration
• Answer generation with cited sources for traceability
• Multiple search modes (speed/balanced/quality) to trade off latency vs depth
• File uploads for document-based Q&A (such as PDFs, text files, and images)
• Image and video search alongside standard web results
• Domain-scoped search to focus results on specific websites
• Smart query suggestions and a local search history
• Built-in API for integrating search and answering into other applications

Use Cases

• Private, self-hosted alternative to Perplexity-style web answering for individuals or teams
• Research assistant that produces source-cited summaries from the open web
• Internal tool that combines uploaded documents with web search for faster troubleshooting

Limitations and Considerations

• Answer quality and latency depend heavily on the chosen model/provider and the availability/quality of web search results
• Some functionality requires external provider API keys when not using a local model

Perplexica is well-suited for users who want a Perplexity-like experience while keeping searches and data under their control. With SearxNG-based search, configurable LLM backends, and citations, it aims to balance privacy, usability, and answer reliability.

Onyx Community Edition is an open-source, self-hostable AI platform that combines a team chat UI with enterprise search and retrieval-augmented generation (RAG). It is designed to work with a wide range of LLM providers as well as locally hosted models, including deployments in airgapped environments.

Key Features

• AI chat interface designed to work with multiple LLM providers and self-hosted LLMs
• RAG with hybrid retrieval and contextual grounding over ingested and uploaded content
• Connectors to many external knowledge sources with metadata ingestion
• Custom agents with configurable instructions, knowledge, and actions
• Web search integration and deep-research style multi-step querying
• Collaboration features such as chat sharing, feedback collection, and user management
• Enterprise-oriented access controls including RBAC and support for SSO (depending on configuration)

Use Cases

• Company-wide AI assistant grounded in internal documents and connected tools
• Knowledge discovery and enterprise search across large document collections
• Building task-focused AI agents that can retrieve context and trigger actions

Limitations and Considerations

• Some advanced organization-focused capabilities may differ between Community and Enterprise editions
• Retrieval quality and permissions mirroring depend on connector availability and configuration

Onyx CE is a strong fit for teams that want an extensible, transparent AI assistant and search layer over internal knowledge. It emphasizes configurable retrieval, integrations, and deployability across diverse infrastructure setups.

Blinko is an open-source, privacy-focused note-taking app designed for quickly capturing short “card” notes and organizing them over time. It adds AI-assisted retrieval using RAG, enabling natural-language search across your personal knowledge base while keeping data under your control.

Key Features

• Card-style note capture optimized for quick, lightweight writing
• Markdown-based notes for simple formatting and portability
• AI-enhanced retrieval using RAG for natural language querying of notes
• Self-hosted data storage emphasizing data ownership and privacy
• Web app built with a modern UI stack
• Optional multi-platform desktop experience via Tauri

Use Cases

• Personal knowledge management with fast capture of ideas and snippets
• Searching a private notes archive using natural-language queries
• Lightweight alternative to heavier note systems for daily journaling and memos

Limitations and Considerations

• AI/RAG features may require additional configuration and external model/provider choices depending on your setup

Blinko fits users who want a clean, fast note workflow with Markdown and the option to add AI-powered retrieval. It is especially suited to individuals prioritizing privacy and control while still benefiting from modern AI search.

Paperless-AI is an AI-powered extension for Paperless‑ngx that automates document classification, metadata extraction and semantic search. It integrates with OpenAI-compatible APIs and local model backends to provide chat-style Q&A over a Paperless‑ngx archive.

Key Features

• Automated document processing: detects new documents in Paperless‑ngx and extracts title, tags, document type, and correspondent.
• Retrieval-Augmented Generation (RAG) chat: semantic search and contextual Q&A across the full document archive.
• Multi-backend model support: works with OpenAI-compatible APIs, Ollama (local models), DeepSeek-r1, Azure and several other OpenAI-format backends.
• Manual review UI: web interface to manually trigger AI processing, review results, and adjust settings.
• Smart tagging and rule engine: configurable rules to control which documents are processed and what tags are applied.
• Docker-first distribution: official Docker image and docker-compose support for containerized deployment and persistent storage.

Use Cases

• Quickly find facts across scanned bills, contracts and receipts via natural-language Q&A instead of manual search.
• Automatically tag and classify incoming documents to reduce manual filing and speed up archival workflows.
• Create structured metadata from free-text documents for downstream automation or reporting.

Limitations and Considerations

• Quality and consistency of automatic tags and correspondents varies by model and prompt; some users report noisy or incorrect tags that require manual cleanup.
• Resource behavior with local model backends (e.g., Ollama) can be heavy; users have reported long-running sessions or elevated GPU/CPU usage depending on model choice and volume.
• Processing can halt on model/API errors (for example, context-length or API failures); robust retry/monitoring may be required in large archives.
• Requires a running Paperless‑ngx instance and appropriate API credentials and model/back-end configuration to operate.

Paperless-AI provides an accessible way to add AI-driven classification and semantic search to a Paperless‑ngx archive, with flexible backend choices and a modern web UI. It is best suited for users who want automated tagging and conversational access to large document collections but should be configured and monitored to manage resource use and tag quality.

Basic Memory is a local-first “memory layer” that lets AI assistants build and reuse long-term context across chats. It stores knowledge as human-editable Markdown files and exposes that knowledge to compatible LLM clients via the Model Context Protocol (MCP).

Key Features

• Bi-directional read/write memory: AI can create and update notes, and you can edit them with standard tools
• Local Markdown storage with semantic patterns (frontmatter, observations, relations) to form a traversable knowledge graph
• Local indexing and search backed by SQLite for fast retrieval
• MCP server integration to connect with compatible AI clients (for example desktop assistants and editors)
• Multi-project organization for separate knowledge bases
• Optional sync workflows, including real-time syncing and cloud-oriented commands

Use Cases

• Build a personal knowledge base that persists across AI conversations without repeated re-explaining
• Maintain project “working memory” for coding, research, or writing using Markdown and wiki-style linking
• Share consistent prompts, instructions, and structured notes across different AI tools while keeping content editable

Limitations and Considerations

• Effectiveness depends on maintaining consistent note structure (observations/relations) for higher-quality retrieval
• Some cross-device features may depend on optional syncing workflows rather than the core local-only setup

Basic Memory is a practical way to turn conversations into durable, structured notes that both humans and AI can navigate. By keeping the source of truth in plain text Markdown, it aims to stay interoperable with existing editors and workflows while enabling richer, reusable AI context.

SecureAI Tools is a self-hosted web app for private AI productivity, focused on AI chat and chatting with your own documents. It can run models locally via Ollama or connect to OpenAI-compatible providers, and includes built-in access controls for multi-user use.

Key Features

• Chat interface for interacting with LLMs
• Document Q&A (PDF support) with offline document processing
• Local model inference via Ollama, with optional GPU acceleration
• Support for remote OpenAI-compatible APIs as an alternative to local inference
• Built-in email/password authentication and basic user management
• Optimized self-hosting experience with Docker Compose and setup scripts
• Integrations including Paperless-ngx and Google Drive

Use Cases

• Private, family or small-team AI assistant with account-based access
• Ask questions and summarize PDFs and organized document collections
• Run local LLMs on a workstation or home server to keep data on-premises

Limitations and Considerations

• Document chat is currently focused on PDFs; broader file-type support is still evolving
• Local inference performance depends heavily on available RAM/GPU, especially on non-Apple systems

SecureAI Tools is a practical option for users who want a privacy-oriented AI chat experience combined with document Q&A, and the flexibility to choose between local models and OpenAI-compatible providers.

I, Librarian is a web-based application for organizing, annotating and sharing collections of PDF papers and office documents. It targets individual researchers and small-to-medium research groups, providing centralized storage, in-browser PDF annotation and advanced full-text search including OCR support. (i-librarian.net)

Key Features

• Centralized library management with multi-user access and project-based collaboration.
• In-browser PDF viewer with multicolor highlighting, pinned/shared notes and exportable annotations.
• Powerful full-text search across metadata, PDF text and annotations with multilingual OCR for scanned documents.
• Import and metadata harvesting from scientific sources (arXiv, PubMed, NASA, IEEE, Crossref, etc.) and citation export (BibTeX/EndNote/etc.).
• Multiple deployment options: hosted service, Docker deployment or manual install; optional integrations such as SSO (OpenID/SAML/LDAP). (i-librarian.net)

Use Cases

• Research labs or departments that need a shared, searchable repository of papers and collaborative annotations.
• Individual academics or students who want a personal reference manager with in-browser annotation and full-text search.
• Institutions that need controlled access to a centrally hosted PDF library with audit and group features. (linuxlinks.com)

Limitations and Considerations

• Self-hosted installations require a PHP-capable web server and a database backend; official instructions reference Apache + PHP 8+, and optional external tools (LibreOffice, Tesseract OCR) for Office import and OCR functionality. Installation and OCR depend on those external components being present and configured. (github.com)

I, Librarian is available as a hosted SaaS or as a GPL-3.0 free edition for self-hosting; the project repository and deployment artifacts (Dockerfile, Caddyfile) are publicly maintained. It is focused on research-oriented PDF management and team collaboration. (github.com)