RLAMA
Local assistant for document question answering using RAG.
Score
60
User Rating
3.0
Free/Trial Support
Supported
Starting Price
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Last Updated
Aug 14, 2026
Overview
What is RLAMA?
RLAMA (Retrieval-Augmented Local Assistant Model Agent) is an open-source AI solution that integrates with local AI models to create, manage, and interact with Retrieval-Augmented Generation (RAG) systems. It allows users to build powerful document question-answering systems with multiple document formats, advanced semantic chunking, and local storage and processing.
What is RLAMA used for?
Tags
Top Features
- Create, manage, and interact with RAG systems
- Support for multiple document formats (.txt, .md, .pdf, etc.)
- Advanced semantic chunking strategies
- Local storage and processing with no data sent externally
- Web crawling to create RAGs directly from websites
- Directory watching for automatic RAG updates
- Hugging Face integration with 45,000+ GGUF models
- HTTP API server for application integration
- Cross-platform support (macOS, Linux, Windows)
- OpenAI model support alongside Ollama
- AI Agents & Crews for specialized tasks
- Visual RAG Builder (RLAMA Unlimited)
How to use RLAMA?
RLAMA can be installed and used via the command line. Users can create RAG systems by indexing folders of documents, query documents in an interactive session, and manage RAG systems with commands like `rlama rag`, `rlama run`, `rlama list`, and `rlama delete`. RLAMA Unlimited offers a visual interface for building RAG systems without coding.
Alternative Tools
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Pros & Cons
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Use Cases
- Query project documentation, manuals, and specifications
- Create secure RAG systems for sensitive documents with full privacy
- Query research papers, textbooks, and study materials for faster learning
RLAMA Pricing
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No detailed pricing information available
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