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Basic Information

Refly.AI is an open-source agentic workspace designed to blend human insight with AI execution for real-world tasks. It is intended for teams, developers, and organizations that want a collaborative platform to prototype product designs, generate presentations, run deep research workflows, create multimodal marketing content, and orchestrate operational pipelines. The repository provides a self-hostable community edition with Docker and Kubernetes deployment instructions and a cloud-hosted option for zero-configuration usage. It targets users who need transparent, controllable AI assistance and supports enterprise private deployments via contact. The project emphasizes human-in-the-loop workflows, multi-threaded conversation contexts, and extensibility so contributors can extend models, connectors, and knowledge sources. Documentation, contribution guidelines, and community channels are provided to help operators and developers deploy, customize, and maintain the workspace.

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Features
Refly.AI centralizes multiple capabilities for agentic workflows. It implements a multi-threaded conversation system for parallel context management and complex agentic workflows. It offers a multi-model integration framework with support for 13+ language models, hybrid scheduling, and parallel processing. Multimodal processing supports PDFs, DOCX, HTML, EPUB, and common image formats with batch analysis. An AI-powered skill system enables web search, vector-based retrieval, and document generation. Context management provides temporary knowledge construction and multi-dimensional correlation. A knowledge base engine supports multi-source import, RAG semantic retrieval, and knowledge graph construction. Additional features include one-click content capture from platforms, a citation system, an AI-enhanced Markdown editor, code artifact generation for web components, and a website visualization engine.
Use Cases
Refly.AI helps teams accelerate tasks that combine human judgment and automated AI work. It reduces friction when prototyping and producing content by supporting multimodal inputs, intelligent content capture, and citation tracking. Developers gain a platform with built-in RAG retrieval, vector database compatibility, and knowledge graph features for maintaining context across sessions. The multi-model framework allows routing work to different language models and running hybrid or parallel model schedules. Deployment options include Docker, Kubernetes, and a managed cloud offering, enabling flexible hosting for small teams or enterprise environments. The editor, code generation, and visualization tools speed iteration on presentations and web prototypes while the plugin and SDK roadmap promises extensibility for custom integrations and autonomous task flows.

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