# llmware-ai/llmware

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14,838 stars · 2,921 forks · Python · Apache-2.0

## Links

- GitHub: https://github.com/llmware-ai/llmware
- Homepage: https://llmware-ai.github.io/llmware/
- awesome-repositories: https://awesome-repositories.com/repository/llmware-ai-llmware.md

## Description

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.

## Tags

### Artificial Intelligence & ML

- [Autonomous Agent Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/autonomous-agent-orchestration.md) — Provides a framework for deploying modular agents with persistent memory to automate complex, multi-step workflows. ([source](https://github.com/llmware-ai/llmware/blob/main/welcome_to_llmware_windows.sh))
- [Local Model Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/local-model-execution.md) — Executes GGUF model files directly on local hardware for private, offline inference without external APIs. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/models/using-open-chat-models.py))
- [Local AI Model Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/model-integration-serving/local-ai-model-runtimes.md) — Integrates specialized model formats and runtimes for efficient local and edge-based execution. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/README.md))
- [Document Grounding](https://awesome-repositories.com/f/artificial-intelligence-ml/context-aware-retrieval/document-grounding.md) — Anchors AI responses to specific evidence found within retrieved document snippets.
- [Embedding Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/embedding-generators.md) — Transforms raw documents into vector representations using embedding models to enable semantic search. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/README.md))
- [Grounded Answer Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai/grounded-answer-generation.md) — Produces grounded AI responses by combining user queries with retrieved source materials and traceable citations. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/embeddings/using_semantic_reranker_with_rag.py))
- [Document Chunking Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/retrieval-augmented-generation/document-chunking-strategies.md) — Provides strategies for segmenting source documents into manageable units to optimize retrieval accuracy within RAG pipelines. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/sources/pdf_parser_new_configs.py))
- [RAG Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/retrieval-augmented-generation/rag-pipelines.md) — Constructs end-to-end RAG pipelines that connect knowledge sources to generative models via document parsing. ([source](https://github.com/llmware-ai/llmware#readme))
- [Local Model Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/local-model-integrations.md) — Connects locally hosted model instances to a unified manageable catalog for application use. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/onnxruntime/using_local_foundry_models.py))
- [Local RAG Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/local-rag-implementations.md) — Implements RAG pipelines using local datasets and offline language models for private document analysis. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/use_cases/contract_analysis_on_laptop_with_bling_models.py))
- [Edge AI Model Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/local-and-on-device-inference/edge-ai-model-deployment.md) — Provides tools to execute fine-tuned AI workloads on private local infrastructure and edge devices. ([source](https://github.com/llmware-ai/llmware/blob/main/setup.py))
- [Quantized Model Deployments](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/local-and-on-device-inference/edge-ai-model-deployment/quantized-model-deployments.md) — Implements quantized inference to allow models to run on local CPU, GPU, and NPU acceleration. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions))
- [Local Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/local-ai-deployment-platforms/deployment-platforms/local-inference.md) — Executes language models on local hardware to generate responses without external API dependencies. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/models/dragon_gguf_fast_start.py))
- [Local Model Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/local-ai-deployment-platforms/local-model-execution.md) — Downloads and caches language models to the local file system for private execution. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/ui/dueling_chatbot.py))
- [Function Calling Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/function-calling-fine-tuning.md) — Utilizes fine-tuned small models to perform extraction and summarization tasks returning programmatic structures. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/models/using_function_calls.py))
- [Model Abstractions](https://awesome-repositories.com/f/artificial-intelligence-ml/model-abstractions.md) — Provides a unified interface to normalize diverse AI model APIs and local model providers.
- [Function Calling Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/model-execution-interfaces/function-calling-interfaces.md) — Triggers specific logic and classification tasks through a model's function-calling interface. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/openvino/multimedia_bot.py))
- [Multi-Agent Coordination](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-agent-coordination.md) — Provides frameworks for synchronizing and coordinating state across multiple autonomous AI agents. ([source](https://llmware-ai.github.io/llmware/))
- [Multi-Model Workflow Coordinators](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-model-workflow-coordinators.md) — Sequences different specialized AI models through logic paths to execute complex research and analysis tasks. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/slim_agents))
- [Quantized Inference Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes.md) — Executes compressed model formats on local hardware using specialized runtimes for CPU, GPU, and NPU acceleration.
- [Question Answering](https://awesome-repositories.com/f/artificial-intelligence-ml/question-answering.md) — Generates accurate answers to specific questions by referencing provided context passages. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/ui/dueling_chatbot.py))
- [Prompt Augmenters](https://awesome-repositories.com/f/artificial-intelligence-ml/retrieval-augmented-generation-pipelines/prompt-augmenters.md) — Injects retrieved document snippets into model prompts to ground generation and enable accurate fact-checking. ([source](https://github.com/llmware-ai/llmware/blob/main/README.md))
- [Semantic Search](https://awesome-repositories.com/f/artificial-intelligence-ml/semantic-search.md) — Provides semantic search capabilities to retrieve contextually relevant information from local libraries for language models. ([source](https://github.com/llmware-ai/llmware/blob/main/welcome_to_llmware_windows.sh))
- [Unified Model Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/speech-to-text-integrations/unified-model-interfaces.md) — Provides a unified interface for inference and streaming across different model implementations. ([source](https://github.com/llmware-ai/llmware/blob/main/README.md))
- [Structured Data Extraction](https://awesome-repositories.com/f/artificial-intelligence-ml/structured-data-extraction.md) — Converts unstructured text into dictionaries of specific keys and values using function-calling. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/use_cases/parsing_great_speeches.py))
- [Structured Data Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/structured-data-generation.md) — Produces machine-readable data such as JSON or SQL using specialized small-parameter models. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/slim_agents))
- [Unified Model Catalogs](https://awesome-repositories.com/f/artificial-intelligence-ml/unified-model-catalogs.md) — Provides a unified model catalog to standardize access to specialized language models regardless of their implementation. ([source](https://github.com/llmware-ai/llmware#readme))
- [Vector Database ETL Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-databases/vector-database-etl-tools.md) — Segments document content into chunks and generates embeddings for storage in vector databases.
- [Small Model Agents](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-architectures/orchestration-engines/ai-agent/small-model-agents.md) — Utilizes small language models as specialized agents to perform specific function-calling tasks. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions))
- [Cross-Hardware Workload Distribution](https://awesome-repositories.com/f/artificial-intelligence-ml/cross-hardware-workload-distribution.md) — Spreads model execution across available CPU, GPU, and NPU hardware to maximize processing efficiency. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/openvino/multimedia_bot.py))
- [Document Rerankers](https://awesome-repositories.com/f/artificial-intelligence-ml/document-rerankers.md) — Prioritizes useful context by scoring and reranking retrieved text segments based on semantic relevance. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/onnxruntime/using_onnx_reranker_models.py))
- [External Model Connectors](https://awesome-repositories.com/f/artificial-intelligence-ml/external-model-connectors.md) — Connects to third-party model APIs via custom base URIs and prompt wrappers. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/models/using-open-chat-models.py))
- [External Service Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/external-service-integrations.md) — Enriches research materials by combining document data with real-time information from third-party APIs. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/use_cases/web_services_slim_fx.py))
- [NPU Inference Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-accelerated-inference/npu-inference-execution.md) — Accelerates language model inference using specialized NPU runtimes on compatible ARM-based hardware. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/onnxruntime/using-qnn-npu-models.py))
- [Image Content Analyzers](https://awesome-repositories.com/f/artificial-intelligence-ml/image-content-analyzers.md) — Analyzes extracted images to detect and convert visual text into searchable strings for document collections. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/use_cases/slicing_and_dicing_office_docs.py))
- [Language Model Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration.md) — Coordinates complex interactions between specialized language models and external tools for multi-stage research.
- [ONNX Runtime Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-engines/onnx-runtime-inference.md) — Integrates the ONNX runtime to provide high-performance, cross-platform inference for generative models. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/onnxruntime/using_onnx_models.py))
- [Incremental Inference Streaming](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/model-integration-pipelines/model-inference/inference-result-processors/incremental-inference-streaming.md) — Sends model output to the client incrementally as a generator for real-time chat interactions. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/gguf/gguf_streaming.py))
- [Model Loading Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-management/model-loading-interfaces.md) — Retrieves and caches pre-configured models to run generative tasks on local hardware. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/ui/gguf_streaming_chatbot.py))
- [Natural Language Query Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-query-generators.md) — Translates natural language questions into executable SQL queries to retrieve structured research output. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/use_cases/agent_with_custom_tables.py))
- [Natural Language to CSV Conversion](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-querying-interfaces/natural-language-to-csv-conversion.md) — Translates plain English queries into structured CSV data by interfacing with SQL databases. ([source](https://github.com/llmware-ai/llmware/blob/main/README.md))
- [Sampling Controls](https://awesome-repositories.com/f/artificial-intelligence-ml/probabilistic-modeling/sampling-controls.md) — Implements sampling controls like temperature to adjust the randomness and creativity of model outputs during inference. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/models/adjusting_sampling_settings.py))
- [Comprehension Question Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/question-answering-systems/multi-hop-question-generators/comprehension-question-generators.md) — Automatically generates open-ended and multiple-choice questions from context to test reading comprehension. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/slim_agents/using-slim-q-gen.py))
- [Sentiment Analysis Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/sentiment-analysis-tools.md) — Executes specialized models for sentiment analysis and topic extraction. ([source](https://github.com/llmware-ai/llmware/blob/main/README.md))
- [Speech to Text Transcription](https://awesome-repositories.com/f/artificial-intelligence-ml/speech-to-text-transcription.md) — Converts voice-to-text files into processed text chunks with timestamps and source references. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/use_cases/parsing_great_speeches.py))
- [Risk Classifications](https://awesome-repositories.com/f/artificial-intelligence-ml/text-classification/risk-classifications.md) — Ships specialized classifier models to detect toxicity, bias, and prompt injection attacks within text. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/openvino/using_openvino_classifier_model.py))
- [Text-to-SQL Translators](https://awesome-repositories.com/f/artificial-intelligence-ml/text-to-sql-translators.md) — Maps natural language queries to structured SQL commands using schema-aware guidance. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/slim_agents))

### Part of an Awesome List

- [Document Parsing and Extraction](https://awesome-repositories.com/f/awesome-lists/data/document-parsing-and-extraction.md) — Processes batches of documents to convert unstructured content into formats suitable for LLM ingestion. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/use_cases/msa_processing.py))
- [Hardware Optimized Inference](https://awesome-repositories.com/f/awesome-lists/ai/hardware-optimized-inference.md) — Optimizes generative model execution on Intel hardware using the OpenVino runtime. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/openvino/using_openvino_models.py))
- [Named Entity Recognition](https://awesome-repositories.com/f/awesome-lists/ai/named-entity-recognition.md) — Extracts specific names and custom keys from text to be used as filters for document retrieval. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/slim_agents))
- [Text Extraction and OCR](https://awesome-repositories.com/f/awesome-lists/more/text-extraction-and-ocr.md) — Processes embedded images using OCR to recover text for indexing and subsequent analysis. ([source](https://github.com/llmware-ai/llmware/blob/main/README.md))
- [Application Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/application-frameworks.md) — Enterprise-grade development framework and tools.
- [Retrieval Augmented Generation](https://awesome-repositories.com/f/awesome-lists/ai/retrieval-augmented-generation.md) — Unified framework for enterprise-grade RAG pipelines.

### Data & Databases

- [Contextual Knowledge Indexers](https://awesome-repositories.com/f/data-databases/contextual-knowledge-indexers.md) — Parses files and creates indexed libraries using embedding models to provide context for AI agents. ([source](https://github.com/llmware-ai/llmware/blob/main/README.md))
- [Natural Language to SQL](https://awesome-repositories.com/f/data-databases/data-visualization-charts/natural-language-querying/natural-language-to-sql.md) — Translates natural language prompts into executable SQL queries by analyzing database schemas.
- [Hybrid Search](https://awesome-repositories.com/f/data-databases/hybrid-search.md) — Executes hybrid search queries combining semantic vector similarity with structured metadata filtering. ([source](https://github.com/llmware-ai/llmware/blob/main/README.md))
- [Hybrid Search Engines](https://awesome-repositories.com/f/data-databases/hybrid-search-engines.md) — Integrates vector-based semantic retrieval with traditional keyword-based indexing and metadata filtering.
- [Text Segmentation](https://awesome-repositories.com/f/data-databases/text-processing-utilities/text-extraction/text-segmentation.md) — Splits text into segments using strategies that preserve words or natural breaks for better retrieval. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/sources/pdf_parser_new_configs.py))
- [Vector Database Integrations](https://awesome-repositories.com/f/data-databases/vector-database-integrations.md) — Integrates with a wide range of SQL and specialized vector databases for high-dimensional data storage. ([source](https://github.com/llmware-ai/llmware#readme))
- [Document Extraction Tools](https://awesome-repositories.com/f/data-databases/document-extraction-tools.md) — Parses and extracts structured elements like images, tables, and headers from complex file formats. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/sources/pdf_parser_new_configs.py))
- [Document Parsing Engines](https://awesome-repositories.com/f/data-databases/document-parsing-engines.md) — Extracts structured text and tables from complex file formats including PDF, Word, PowerPoint, and Excel. ([source](https://github.com/llmware-ai/llmware/blob/main/README.md))
- [Full Text Search](https://awesome-repositories.com/f/data-databases/full-text-search.md) — Enables retrieval of specific pages or sources from libraries using lexical and keyword-based text queries. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/use_cases/msa_processing.py))
- [PDF Parsers](https://awesome-repositories.com/f/data-databases/pdf-parsers.md) — Parses PDF files into libraries with configurable options for encoding and header removal. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/sources/pdf_parser_new_configs.py))
- [Multi-Pass Query Strategies](https://awesome-repositories.com/f/data-databases/search-indexing/complex-search-querying/multi-pass-query-strategies.md) — Retrieves precise document snippets by combining multiple search methods with custom attribute filters. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/sources/dual_pass_with_custom_filter.py))
- [Text Search](https://awesome-repositories.com/f/data-databases/text-search.md) — Implements fast inline text searching across parsed dictionaries to isolate specific data segments. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/use_cases/parsing_great_speeches.py))

### Development Tools & Productivity

- [Agent-Integrated Functions](https://awesome-repositories.com/f/development-tools-productivity/local-function-execution/agent-integrated-functions.md) — Implements functions triggered by agents to perform data transformations and external actions during conversational loops.

### Content Management & Publishing

- [Office Document Parsers](https://awesome-repositories.com/f/content-management-publishing/content-processing-transformation/document-processing-conversion/document-processing/format-specific-parsers/office-document-parsers.md) — Extracts structured text and data from Microsoft Word, PowerPoint, and Excel files. ([source](https://github.com/llmware-ai/llmware/tree/main/solutions/sources/office_parser_new_configs.py))

### Education & Learning Resources

- [Citation Integrity Verification](https://awesome-repositories.com/f/education-learning-resources/academic-citations/citation-integrity-verification.md) — Verifies the factual accuracy of responses by cross-referencing claims against source documents and citations. ([source](https://github.com/llmware-ai/llmware/blob/main/solutions/ui/simple_rag_ui_with_streamlit.py))