LLMWare (14,861 Stars) 2026: Enterprise LLM Platform for RAG Workflows and Document Intelligence

LLMWare (14,861 stars) is an enterprise-grade LLM platform for RAG workflows, document processing and knowledge management. Here is how to build a document Q&A pipeline with it.

💡 What You Will Learn

LLMWare (14,861 stars) is an enterprise-grade LLM platform for RAG workflows, document processing and knowledge management. Here is how to build a document Q&A pipeline with it.

## The short answer **llmware-ai/llmware** (14,861 stars, Python) is an enterprise-grade LLM development framework for RAG workflows. It provides a full toolkit - document parsing, embedding, vector storage, model abstraction, and knowledge-retrieval agents - designed to run on-prem or in the cloud with your own models. ## What it focuses on - **Document intelligence**: parse PDFs, Word, Excel, and email at scale - **RAG pipelines**: chunk, embed, store, and retrieve with a simple API - **Model abstraction**: swap between local and cloud LLMs - **Knowledge agents**: retrieval agents that cite sources - **Enterprise-friendly**: on-prem deployment, no vendor lock-in ## Build a document Q&A pipeline ```bash pip install llmware ``` ```python from llmware.library import Library from llmware.retrieval import Query # Create a library from your documents library = Library().create_new_library("contracts") library.add_files(input_folder_path="/path/to/pdfs") # Query with a model query = Query(library) results = query.text_query("termination clause", result_count=5) for r in results: print(r["text"][:200]) ``` ## Adding an LLM for generative answers ```python from llmware.models import ModelCatalog model = ModelCatalog().load_model("llmware/bling-1b-0.1") response = model.function_call( "Summarize the key obligations in this contract", context=results ) print(response["llm_response"]) ``` ## Practical tips - Start with the built-in Bling models for fast, low-cost extraction. - Organize documents into separate libraries by domain for cleaner retrieval. - Use the built-in OCR for scanned PDFs. ## FAQ **Is it free?** Yes - Apache-2.0 open source. **Does it need a GPU?** Small models run on CPU; larger models benefit from GPU. **Can I use my own LLMs?** Yes - it supports local models, Hugging Face, and OpenAI-compatible endpoints.