AI-native HTAP database with Git-for-Data and built-in vector search, serving as the data and memory backbone for intelligent agents and applications.

The PHP Agentic Framework to build production-ready AI driven applications. Connect components (LLMs, vector DBs, memory) to agents that can interact with your data. With its modular architecture it's best suited for building RAG, multi-agent workflows, or business process automations.

A lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents. Say goodbye to Vector RAG and amnesia. Empower your AI with persistent, graph-like structured memory across any model, session, or tool. Drop-in replacement for OpenClaw.

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

Class notes for the course "Long Term Memory in AI - Vector Search and Databases" COS 597A @ Princeton Fall 2023

A completely private, locally-operated Ai Assistant/Chatbot/Sub-Agent Framework with realistic Long Term Memory and thought formation using Open Source LLMs. Qdrant is used for the Vector DB.

BondAI is an open-source tool for developing AI Agent Systems. BondAI handles the implementation complexities including memory/context management, error handling, vector/semantic search and includes a powerful set of out of the box tools and integrations.

Suite of tools containing an in-memory vector datastore and AI proxy

Comprehensive MCP server exposing dozens of capabilities to AI agents: multi-provider LLM delegation, browser automation, document processing, vector ops, and cognitive memory systems

🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.

aivectormemory 是一款基于 Model Context Protocol (MCP) 开发的OpenClaw、OpenCode、ClaudeCodeAI记忆管理工具。它专门为 Claude、OpenCode、Cursor 和 主流IDE 编程工具设计，通过向量数据库技术解决 AI 在不同对话会话中「健忘」的问题。aivectormemory: A lightweight MCP Server enabling persistent, cross-session memory for AI-powered IDEs via vector search.

Zettelkasten-based persistent memory for AI coding agents. Works with Claude Code, Cursor, VS Code Copilot, Codex, Windsurf & any MCP client. No vector DB — just markdown + git sync.

A hybrid programming language combining Lean4's formal verification with blazing-fast compilation, actor-based agent orchestration, AI-driven optimization, and vector-backed agent memory.

Lightweight, local-first memory for AI agents. Hybrid vector + BM25 search, LLM-powered fact extraction, zero infrastructure — just pip install.

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

Dual-layer memory for AI agents. Compressed index + vector store. 91% recall, 70ms, fully local.

Dragon Brain — persistent long-term memory for AI agents via MCP (Model Context Protocol). Knowledge graph (FalkorDB) + vector search (Qdrant) + CUDA GPU embeddings. Works with Claude, Gemini CLI, Cursor, Windsurf, VS Code Copilot. 30 tools, 1121 tests.

It is a simple, fast, and hard-durable embedded database designed specifically for AI agent memory. It provides a single-file-like experience (no server required) but with native support for vectors, graphs, and temporal search.

Local character AI chatbot with chroma vector store memory and some scripts to process documents for Chroma

🧠 The First Brain for OpenClaw — Persistent vector memory for AI agents. PostgreSQL + pgvector + OpenAI embeddings. One brain, every agent, shared intelligence.

An explainable AI system that combines Graph Intelligence, Vector Search, and Retrieval-Augmented Generation (RAG) to deliver grounded answers and transparent reasoning paths. Includes a FastAPI backend, Streamlit UI, FAISS vector index, and an in-memory knowledge graph for hybrid retrieval and recommendations.

SochDB is a high-performance embedded, ACID-compliant vector database purpose-built for AI agents and memory

VexFS is a Linux kernel-native file system with built-in vector search and semantic memory. Designed for AI agents, RAG, and LLM workloads, it merges POSIX storage with vector-native indexing. Built in Rust, bootable via QEMU, and engineered for AGI infrastructure.

🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.

Define AI tools in YAML with natural language schemas. All tool usage is automatically stored in Qdrant vector database, enabling semantic search, filtering, and memory retrieval across sessions.

A high-performance, in-memory vector database written in Rust, designed for semantic search and top-k nearest neighbor queries in AI-driven applications, with binary file persistence for durability.

FastMemory is a topological representation of text data using concepts as the primary input. It helps in improving the RAG(by replacing embedding and vectors entirely), AI memory and LLM queries by upto 100% as in the huggingface benchmarks(22+ SOTA)

A production-ready memory-enhanced AI agent that gives Large Language Models persistent conversational memory using a ChromaDB vector database. Retrieves relevant past interactions via semantic similarity to generate context-aware responses across sessions.

🧠 Stop building AI that forgets. Master MCP (Model Context Protocol) with production-ready semantic memory, hybrid RAG, and the WARNERCO Schematica teaching app. FastMCP + LangGraph + Vector/Graph stores. Your AI assistant's long-term memory starts here.

The memory system your AI agent deserves. 4-stage hybrid retrieval — Vector + BM25 + Knowledge Graph + Neural Reranker — in <150ms. Self-hosted, $0/query, built for agents that need to actually remember.