8 Best Chat with your enterprise data using LLM Alternatives in 2026 (Open Source)

Chat with your enterprise data using LLM — Chat and Ask on your own data. Accelerator to quickly upload your own enterprise data and use OpenAI services to chat to that uploaded data and ask questions. vs simple PDF chatbots: enterprise Azure-native document AI platform with SQL agents, PromptFlow evaluation, speech integration, function calling, and session persistence — the most feature-rich Azure OpenAI reference implementation

These 8 open-source tools do the same job. They are ordered by how closely they match Chat with your enterprise data using LLM, with live GitHub data so you can see which projects are actively maintained.

1. 1. ragflow

   RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs

   What sets it apart: Unlike LlamaIndex (framework, assemble-yourself) or AnythingLLM (desktop all-in-one), RAGFlow is a purpose-built enterprise RAG engine with deep document understanding (OCR, table extraction, layout analysis), template-based chunking with human visualization, and grounded citations — focused on quality-in-quality-out for complex enterprise documents.

   Best for: Enterprises needing production RAG with deep document parsing, grounded citations, and traceable answers; Organizations with complex document types (scanned PDFs, tables, mixed formats) requiring high-fidelity extraction

   ⭐ 91.5kChat with your enterprise data using LLM vs ragflow →Full profile →

2. 2. private-gpt

   Interact with your documents using the power of GPT, 100% privately, no data leaks

   What sets it apart: vs LocalGPT / other private RAG: production-ready OpenAI-compatible API with LlamaIndex backend, dependency injection architecture, and enterprise upgrade path via Zylon — canonical repo (zylon-ai/private-gpt) for PrivateGPT

   Best for: Regulated industries needing fully private document Q&A (healthcare, legal, finance); Teams wanting an OpenAI-compatible API for private RAG; Developers building private AI apps with production-ready primitives

   ⭐ 57.6kChat with your enterprise data using LLM vs private-gpt →Full profile →

3. 3. private-gpt

   Interact with your documents using the power of GPT, 100% privately, no data leaks

   What sets it apart: 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

   Best for: Regulated industries needing fully private document Q&A (healthcare, legal, finance); Teams wanting an OpenAI-compatible API for private RAG; Developers building private AI apps with production-ready primitives

   ⭐ 57.6kChat with your enterprise data using LLM vs private-gpt →Full profile →

4. 4. 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

   ⭐ 66.6kChat with your enterprise data using LLM vs AnythingLLM →Full profile →

5. 5. DB-GPT

   open-source agentic AI data assistant for the next generation of AI + Data products.

   What sets it apart: Full-stack AI data assistant combining autonomous SQL generation, sandboxed code execution, and reusable skills in a single platform — not just a chatbot

   Best for: Data teams needing natural language database querying; Organizations wanting AI-powered data analysis assistants; Teams building data-driven agent workflows

   ⭐ 20.1kChat with your enterprise data using LLM vs DB-GPT →Full profile →

6. 6. 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

   ⭐ 1.6kChat with your enterprise data using LLM vs knowledge_gpt →Full profile →

7. 7. 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

   ⭐ 320Chat with your enterprise data using LLM vs DataChad →Full profile →

8. 8. DocsGPT

   Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.

   Best for: Enterprise teams building private document Q&A systems; Organizations needing on-premise AI deployment with data privacy control; Teams requiring multi-format document ingestion including audio workflows

   ⭐ 18.3kChat with your enterprise data using LLM vs DocsGPT →Full profile →