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.

📜 Table of Contents

• The short answer
• What it focuses on
• Build a document Q&A pipeline
• Adding an LLM for generative answers
• Practical tips
• FAQ

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

pip install llmware

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

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.

❓ FAQ

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.