llmware-ai/llmware

Health v2 · maintenance only

Adoption not part of the score

14850 stars · 2943 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

llmware is a Python framework for building enterprise RAG pipelines and LLM-based applications using small, specialized open-source models. It combines a catalog of 300+ prepackaged models (GGUF, ONNX, OpenVINO) with integrated document parsing, knowledge base creation, and agent orchestration, optimized for local on-device deployment.

Use cases

• build a RAG pipeline over enterprise documents
• run LLM inference locally on a laptop or AI PC
• parse PDFs and office documents into a searchable knowledge base
• orchestrate multi-step agent workflows with small models
• query private documents with a local LLM without sending data to the cloud
• fine-tune or use specialized small models for enterprise task automation

When to choose

• you need private, on-premise or on-device LLM applications with no data leaving the machine
• you want an all-in-one framework covering parsing, retrieval, prompting, and agents
• you want to run quantized small models efficiently across Windows, Mac, and Linux with GPU/NPU support

When to avoid

• you only need a thin wrapper around a single cloud LLM API
• you require massive-scale distributed training or serving of large foundation models
• you need a framework tied to a specific stack like LangChain or a managed vector database

Facets

framework · maturity active

rag llm-inference agent-framework parser prompt-engineering machine-learning chatbot large-language-models developer-tools self-hosted python windows self-hosted small-language-models gguf onnx openvino document-parsing enterprise-ai local-inference llamacpp vector-search retrieval-augmented-generation ai-agents natural-language-processing macos linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="llmware-ai/llmware")