llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation.
The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural language queries into structured SQL or CSV formats by analyzing database schemas.
The framework covers a broad range of capabilities including end-to-end retrieval-augmented generation pipelines, hybrid search engines, and multimodal content processing for PDFs, Office documents, audio, and images. It also incorporates tools for structured function calling, named entity recognition, and text risk classification to detect toxicity and prompt injections.
The system integrates with various SQL and vector database backends to manage knowledge collection indexing and document embeddings.
