Today's RAG & Vector Databases: Fastest-Growing Projects — May 01, 2026
Today's the RAG & Vector Databases space, we're seeing a surge in innovative tools that leverage Retrieval-Augmented Generation (RAG) and vector databases to enhance knowledge graph capabilities, threat intelligence analysis, and full-stack AI development. The top-growing repositories are showcasing the versatility of these technologies in various applications, from cybersecurity to document organization. Notably, several projects are focusing on integrating RAG with other AI tools and frameworks.
Rolandpg/zettelforge takes the lead with a growth score of 13.02 and 33 stars, demonstrating significant interest in its agentic memory for CTI in Python. This repository provides STIX knowledge graphs, threat-actor alias resolution, offline-first RAG, and MCP server capabilities for Claude Code and LangChain agents, making it an attractive solution for developers seeking to enhance their cybersecurity tools.
Ais1on/CTI-RAG boasts 186 stars, despite a lower growth score of 5.85, indicating established popularity and recognition in the community. This framework integrates knowledge graph and causal reasoning capabilities to provide security analysts with an intelligent threat intelligence analysis tool, showcasing its value in the CTI space.
Yanhua1010/zero-to-ai-fullstack has garnered attention with a growth score of 4.52 and 151 stars, as it chronicles a Java backend engineer's journey learning AI full-stack development in public, incorporating Python, FastAPI, RAG, pgvector, and Next.js. This repository serves as a valuable resource for developers looking to expand their skills in AI development.
Lastly, nashsu/llm_wiki has achieved an impressive 5,211 stars, with a growth score of 4.32, reflecting its widespread appeal as a cross-platform desktop application that transforms documents into an organized, interlinked knowledge base using LLMs. By leveraging incremental RAG and persistent wiki-building capabilities, this tool streamlines document organization and offers a novel approach to knowledge management.
Overall, these repositories highlight the exciting developments in the RAG & Vector Databases space, with applications spanning cybersecurity, AI development, and document organization. As these tools continue to grow and mature, we can expect to see even more innovative solutions emerge in this rapidly evolving landscape.
Rolandpg/zettelforge takes the lead with a growth score of 13.02 and 33 stars, demonstrating significant interest in its agentic memory for CTI in Python. This repository provides STIX knowledge graphs, threat-actor alias resolution, offline-first RAG, and MCP server capabilities for Claude Code and LangChain agents, making it an attractive solution for developers seeking to enhance their cybersecurity tools.
Ais1on/CTI-RAG boasts 186 stars, despite a lower growth score of 5.85, indicating established popularity and recognition in the community. This framework integrates knowledge graph and causal reasoning capabilities to provide security analysts with an intelligent threat intelligence analysis tool, showcasing its value in the CTI space.
Yanhua1010/zero-to-ai-fullstack has garnered attention with a growth score of 4.52 and 151 stars, as it chronicles a Java backend engineer's journey learning AI full-stack development in public, incorporating Python, FastAPI, RAG, pgvector, and Next.js. This repository serves as a valuable resource for developers looking to expand their skills in AI development.
Lastly, nashsu/llm_wiki has achieved an impressive 5,211 stars, with a growth score of 4.32, reflecting its widespread appeal as a cross-platform desktop application that transforms documents into an organized, interlinked knowledge base using LLMs. By leveraging incremental RAG and persistent wiki-building capabilities, this tool streamlines document organization and offers a novel approach to knowledge management.
Overall, these repositories highlight the exciting developments in the RAG & Vector Databases space, with applications spanning cybersecurity, AI development, and document organization. As these tools continue to grow and mature, we can expect to see even more innovative solutions emerge in this rapidly evolving landscape.