8 Best private-gpt Alternatives in 2026 (Open Source)
private-gpt — Interact with your documents using the power of GPT, 100% privately, no data leaks. vs LocalGPT / other private RAG: production-ready OpenAI-compatible API with LlamaIndex backend, dependency injection architecture, and enterprise upgrade path via Zylon — the most mature private document AI platform
These 8 open-source tools do the same job. They are ordered by how closely they match private-gpt, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| private-gpt(original) | 57.6k | +56 | 2026-09-21 |
| localGPT | 22.2k | +-4 | 2026-08-21 |
| AnythingLLM | 66.6k | +1,564 | 2026-09-30 |
| knowledge_gpt | 1.6k | +-4 | 2023-09-18 |
| DataChad | 320 | +-1 | 2024-02-09 |
| Verba | 7.7k | +13 | 2026-06-08 |
| Quivr | 39.6k | +80 | 2025-06-19 |
| Doc Search | 598 | +0 | 2023-02-18 |
| RAGapp | 4.4k | +6 | 2024-11-04 |
1. localGPT
Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
What sets it apart: vs PrivateGPT / other local RAG: hybrid search engine (semantic + keyword + Late Chunking) with smart query routing and independent answer verification — pure Python, minimal framework dependencies
Best for: Privacy-sensitive document Q&A where no data can leave the premises; Enterprise document intelligence with hybrid search and verification; Developers wanting a modular, extensible local RAG platform
2. AnythingLLM
The all-in-one AI productivity accelerator. On device and privacy first with no annoying setup or configuration.
What sets it apart: Unlike Open WebUI (chat-only) or RAGFlow (enterprise RAG focus), AnythingLLM is the most complete all-in-one desktop AI app combining RAG, no-code agent builder, MCP compatibility, multi-user support, and embeddable widgets — requiring zero coding to set up a private AI workspace.
Best for: Non-technical users who want a private, all-in-one ChatGPT replacement with document chat and agents; Small teams needing a self-hosted multi-user AI workspace with RAG and agent capabilities
3. knowledge_gpt
Accurate answers and instant citations for your documents.
What sets it apart: vs ChatPDF/Unstructured: simple Streamlit-based document Q&A with citation extraction — optimized for quick single-document analysis with verifiable source references
Best for: Extracting cited answers from research papers and reports; Quick document Q&A with source verification; Prototyping RAG-based document analysis tools
4. DataChad
Ask questions about any data source by leveraging langchains
What sets it apart: vs generic RAG chatbots: combines vector embeddings with Smart FAQ curation and context display — shows exactly which chunks informed each answer for transparency
Best for: Quick knowledge base creation from documents and URLs; Conversational Q&A over custom datasets; Building intelligent FAQ systems from existing content
5. Verba
Retrieval Augmented Generation (RAG) chatbot powered by Weaviate
What sets it apart: vs LangChain RAG / LlamaIndex: Weaviate's official RAG application with 8+ chunking strategies, hybrid search, 3D visualization, and multi-provider model support — a complete UI-driven RAG experience rather than a framework
Best for: Building personal knowledge bases with flexible data ingestion; Teams wanting customizable RAG with multiple model providers; Document analysis requiring semantic + keyword hybrid search
6. Quivr
Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore:
What sets it apart: YC-backed 'second brain' RAG framework that prioritizes simplicity (5 lines of code to start) and opinionated defaults over flexibility — same project as quivr
Best for: Building personal knowledge assistants with minimal code; Teams wanting a quick RAG setup over their documents
7. Doc Search
Converse with book - Built with GPT-3
What sets it apart: vs ChatPDF / book-gpt: OCR-based PDF extraction (handles scanned documents) with optional fully local pipeline using HuggingFace models — no cloud dependency required
Best for: Conversational Q&A over scanned or complex PDF documents; Users wanting local/offline document Q&A with HuggingFace models; Researchers needing to query academic papers or books interactively
8. RAGapp
The easiest way to use Agentic RAG in any enterprise
Best for: Enterprise teams needing self-hosted RAG with simple configuration UI; Organizations with data privacy requirements who can't use cloud AI services; Teams wanting OpenAI custom GPT-like experience on their own infrastructure