Llmware
Unified framework for building enterprise RAG pipelines with small, specialized models
Last verified:
What is Llmware?
llmware is an integrated open-source framework designed for building enterprise-grade Retrieval Augmented Generation (RAG) pipelines and LLM-based applications using small, specialized models. The toolkit provides over 50+ open-source models (including BLING, DRAGON, and SLIM series) fine-tuned for enterprise tasks like fact-based question-answering, classification, summarization, extraction, and multi-step agent workflows. It serves everyone from beginners to sophisticated AI developers who want to rapidly build industrial-grade, knowledge-based enterprise LLM applications.
Key features include a unified Model Catalog for accessing 150+ models regardless of implementation (GGUF, HuggingFace, Sentence Transformers, API-based), a Library system for ingesting and indexing documents (PDF, PPTX, DOCX, XLSX, TXT, CSV, MD, JSON, WAV, PNG, JPG, HTML), flexible query capabilities (text, semantic, hybrid, metadata filters), and Prompt with Sources functionality for combining knowledge retrieval with LLM inference. The framework supports multi-model agents with SLIM models for function calls and complex orchestration, runs primarily on CPU/laptop without GPU requirements, and offers simple-to-scale database options including SQLite, ChromaDB, MongoDB, Milvus, Postgres/PGVector, and more.
llmware is ideal for enterprise teams building private, secure RAG systems that connect to internal knowledge sources without cloud dependency. The Apache-2.0 licensed project is maintained by AI Bloks (founded October 2022 by Namee Oberst and Darren Oberst), which also offers a commercial SaaS RAG service. The framework emphasizes cost-effectiveness with small 1-7B parameter models that can run locally, no token costs, and complete data privacy within enterprise security zones.
Llmware pricing
Pricing model: Freemium
The llmware open-source framework is completely free under Apache-2.0 license with no subscription fees. All 50+ specialized models (BLING, DRAGON, SLIM series) are free to download and use. The company AI Bloks offers a commercial SaaS RAG service with paid enterprise plans (pricing not publicly disclosed). The companion product Model HQ offers a free Starter Version with on-device AI execution - $0 expected per-token incremental cost for running models on AI PCs. No token charges or cloud dependency costs for the open-source framework.
Llmware pros
- Open-source with Apache-2.0 license - no cost to use
- 50+ small specialized models fine-tuned for enterprise tasks
- RAG-optimized BLING models (1-3B parameters) run on laptop CPU
- DRAGON 7B GGUF models for state-of-the-art RAG performance
- SLIM models enable multi-model agent function calls
- Model Catalog provides unified access to 150+ models
- Supports GGUF, HuggingFace, Sentence Transformers, and API models
- Universal document ingestion - PDF, PPTX, DOCX, XLSX, CSV, JSON, HTML, images, audio
- Multiple embedding options - mix and match models on same library
- 10 vector database支持 - Milvus, ChromaDB, FAISS, Pinecone, Qdrant, Redis, Neo4j, PGVector, LanceDB, Mongo-Atlas
- 3 text collection databases - MongoDB, Postgres, SQLite
- Fast Start examples with 100+ copy-paste-ready recipes
- No GPU required - most examples run on laptop CPU
- Built-in fact-checking and evidence verification
- Prompt with Sources automatically batches context to fit model window
- Web search and tool integration for agent workflows
- Voice transcription with WhisperCPP support
- OCR for embedded document images
- Text2SQL with Slim-SQL for natural language to CSV
- Streamlit UI examples for building interfaces
Llmware cons
- Primarily Python-based - limited non-Python support
- Steep learning curve for beginners without coding experience
- Requires manual database setup for production (MongoDB, Milvus, Postgres)
- Documentation scattered across GitHub, docs site, and YouTube
- No official cloud-hosted version - self-hosted only for open-source
- Limited Windows support for some compiled libraries
- Active development - breaking changes between versions
- Smaller community compared to LangChain or LlamaIndex
- GGUF models require 16GB+ RAM for optimal performance
- No built-in authentication/authorization - must implement separately
Frequently asked questions about Llmware
What is llmware and what does it do?
llmware is an integrated open-source framework for building enterprise RAG pipelines and LLM applications using small, specialized models. It provides over 50+ fine-tuned models, a unified Model Catalog, document ingestion/parsing/embedding tools, flexible query options, and prompt-with-sources functionality. The framework enables developers to rapidly build knowledge-based enterprise LLM applications that run privately on-premises without cloud dependency.
Do I need a GPU to run llmware?
No, most llmware examples can run on a laptop CPU without a GPU server. The BLING models (1-3B parameters) and GGUF quantized models are specifically optimized for CPU-based inference. GGUF models generally load faster and work well on laptops with at least 16GB of RAM. GPU is optional for larger models or production-scale deployments.
What documents can llmware parse?
llmware supports universal file ingestion with automatic routing to correct parsers by file extension. Supported formats include PDF, PPTX, DOCX, XLSX, TXT, CSV, MD, JSON/JSONL, WAV (audio), PNG, JPG (images), and HTML. The framework also includes OCR capabilities for extracting text from embedded document images.
What vector databases does llmware support?
llmware supports 10 vector databases: Milvus, PGVector (Postgres), Neo4j, Redis, Mongo-Atlas, Qdrant, FAISS, LanceDB, ChromaDB, and Pinecone. For fast start with no installations required, you can use Milvus Lite or ChromaDB which are file-based. You can also install multiple embeddings on the same library using different vector databases.
What are SLIM models and what are they used for?
SLIM (Small, Local, Integrated Models) are small, specialized models fine-tuned for function calling and multi-step agent workflows. They enable multi-model agents that can perform sentiment analysis, extraction, topic detection, boolean queries, summarization, and tool calls. SLIM models are delivered as fast, quantized tools that run on CPU and are accessed through ModelCatalog().get_llm_toolkit().
What are BLING models?
BLING models are RAG-optimized small language models (1-3B parameters) designed for laptop-based CPU inference. The series includes bling-1b-0.1 (most popular), bling-tiny-llama-v0 (fastest), bling-1.4b-0.1, bling-falcon-1b-0.1, bling-cerebras-1.3b-0.1, bling-sheared-llama variants, bling-red-pajamas-3b-0.1, bling-stable-lm-3b-4e1t-v0, and bling-phi-3 (most accurate and newest). GGUF quantized versions are also available for faster loading.
How do I install llmware?
Install llmware using pip: 'pip3 install llmware' for core install or 'pip3 install llmware[full]' for full install with additional dependencies. Alternatively, clone the repository with 'git clone [email protected]:llmware-ai/llmware.git' and run the welcome_to_llmware.sh script (Mac/Linux) or welcome_to_llmware_windows.sh (Windows) to set up requirements, copy examples, and run a welcome_example.py to get started.
What is the Fast Start example series?
Fast Start is a structured set of 6 examples requiring no database installation to learn main RAG concepts with llmware. Each example has extensive comments and a supporting YouTube video. Top examples include: BLING models fast start, Parse and Embed 500 PDFs, Hybrid Retrieval, Multiple Embeddings with PGVector, DRAGON GGUF Models, RAG with BLING on laptop, MSAs with DRAGON, Streamlit UI, LM Studio integration, and Prompts With Sources.
Is llmware suitable for production enterprise use?
Yes, llmware is designed for industrial-grade enterprise LLM applications. It supports scalable database options from laptop (SQLite/ChromaDB) to parallelized clusters (MongoDB/Milvus, Postgres). Features include enterprise-focused models fine-tuned for business processes, private/on-premises deployment with no cloud dependency, Apache-2.0 license for commercial use, and comprehensive tools for the full RAG lifecycle. AI Bloks also offers a commercial SaaS RAG service for enterprises.
How does llmware handle 'not found' answers in RAG?
llmware addresses the 'hardest problem in RAG' - handling 'not found' - through several mechanisms. The framework includes evidence checking with prompt.evidence_check_sources() to fact-check responses after inference. Prompts with Sources automatically packages retrieved context into model-ready batches. The system can filter by document, apply exact mode matching, and use hybrid retrieval combining semantic and text search. There are dedicated tutorials and videos on optimizing RAG prompt accuracy and handling 'not found' scenarios.