Graph_RAG is a Python-based framework designed to build and query knowledge graphs for retrieval-augmented generation (RAG). It supports ingestion of unstructured documents, automated extraction of entities and relationships using LLMs or NLP tools, and storage in graph databases such as Neo4j. With Graph_RAG, developers can construct connected knowledge graphs, execute semantic graph queries to identify relevant nodes and paths, and feed the retrieved context into LLM prompts. The framework provides modular pipelines, configurable components, and integration examples to facilitate end-to-end RAG applications, improving answer accuracy and interpretability through structured knowledge representation.

Manus JS is a client-side JavaScript library that embeds an intelligent research assistant into any web page. It scans and understands HTML content, then uses AI to generate summaries of articles, answer specific questions with references, extract named entities, and produce citation lists in multiple formats. Developers can configure custom prompts, data sources, and UI components to match branding. Manus JS works offline on cached content and connects to AI APIs for live research, making it suitable for academic portfolios, news aggregation sites, or knowledge base platforms seeking to empower users with instant insights.

Ducky empowers teams to build, train, and deploy custom AI agents without writing code. You can ingest documents, spreadsheets, or CRM records as knowledge sources and configure intent recognition, entity extraction, and multi-step workflows via a drag-and-drop interface. Ducky supports integration with REST APIs, databases, and webhooks, and offers multi-channel deployment through web chat widgets, Slack, and Chrome extension. Real-time analytics give insights into conversation volume, user satisfaction, and agent performance. Role-based access controls and versioning ensure enterprise-grade governance while maintaining rapid iteration cycles.