Best AI Agent Skills for Vector Database

LangChain4j is an open-source Java library that simplifies the integration of LLMs into Java applications through a unified API, providing access to popular LLMs and vector databases. It makes implementing RAG, tool calling (including support for MCP), and agents easy. LangChain4j integrates seamlessly with various enterprise Java frameworks.

Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).

AI Agent Development Platform - Supports multiple models (OpenAI/DeepSeek/Wenxin/Tongyi), knowledge base management, workflow automation, and enterprise-grade security. Built with Flask + Vue3 + LangChain, featuring one-click Docker deployment.

Open multilingual construction cost database for AI Agents - 55K+ work items, 27K+ resources, 9 languages. Semantic search via Qdrant vector DB

This template demonstrates how to create a collaborative team of AI agents that work together to process, analyze, and generate insights from documents.

Enterprise-grade (40m+ lines) codebase intelligence in a zero-setup, private and local Claude Plugin or MCP: managed indexing, hybrid semantic search, polyglot code dependency graphs, and DB/API/infra knowledge. Benchmark: 61% less tokens, 84% fewer calls, 37x faster than standard AI grep.

AutoMem is a graph-vector memory service that gives AI assistants durable, relational memory:

A Python library powered by Language Models (LLMs) for conversational data discovery and analysis.

Code from tutorials presented on the "Code AI with Rok" YouTube channel

NornicDB is a low-latency graph + vector, MVCC database with sub-ms writes, and sub 10ms HNSW search + graph traversal, uses Neo4j drivers (Bolt/Cypher) and qdrant's gRPC drivers so you can switch with no changes, then adding intelligent features like LLM inference, embeddings, HNSW+rerank search, GPU acceleration, Auto-TLP, Memory Decay, and MCP