#vector

Self-hosted long-term memory for AI agents. An MCP server that gives agents persistent, hierarchical recall (L0–L3) over pgvector — retain preferences, decisions and observations, then recall them by topic across sessions. MIT, clone-and-run, requires Postgres + pgvector.

About AIKD (AI Knowledge Daemon) is an MCP tool provider written in Rust that gives AI agents instant access to your codebase. It indexes project files into a searchable knowledge base and exposes 7 tools via the Model Context Protocol (MCP) that any AI agent can call. Use Cases AI-assisted development: Let Claude, Cursor, Cline, or any MCP-capable AI search your codebase semantically Code navigation: Find functions, patterns, and logic across large projects in milliseconds Knowledge management: Index documentation, configs, and code into a unified searchable knowledge base Conversation memory: Save and recall conversation context across sessions CI/CD integration: Use CLI or REST API for automated code search in pipelines

Local-first MCP server for searching private Obsidian vaults with hybrid full-text, fuzzy, semantic, and wikilink graph retrieval.

Documentation and assembly review for @nhtio/adk.

MemClaw — persistent memory for AI agent fleets (OSS)

Tribal captures the engineering knowledge that does not get written down in code or tickets. The reasoning behind a load-bearing decision, the heuristic someone keeps reaching for, the breakthrough that closed a gnarly bug. It runs as an MCP server, ingests text on demand, and exposes a graph of items connected by what they support, contradict, or refine. Your agent harness talks to it the same way it talks to any other MCP-compatible tool.

Auditable, self-improving knowledge & memory for AI agents, served over MCP. Citation-enforced answers (no source, no claim), a replayable why-trace per answer, and a judge-gated loop that promotes validated knowledge. Zero-key local embeddings (bge-m3), hybrid vector + BM25 retrieval, scoped fail-closed bearer auth, and multi-tenant row-level security — self-hosted on Postgres alone. Apache-2.0. Reference implementation of the Agentic Product Standard.

MCP-native memory layer for Claude Code, Cursor, Cline, Continue, and 16 other AI tools. Hybrid search (BM25 + pgvector + graph), self-hosted on Supabase + Vercel, 100% MIT, no paywall.

Forge is Voxell's hosted text-embedding API. This MCP server exposes two tools — embed (turn text into vectors) and list_models — so any MCP-compatible agent can generate embeddings for semantic search, RAG, clustering, and dedup. Three tiers let you dial quality against cost: turbo (1024d, fast and cheap), pro (2560d), and ultra (4096d — Qwen3-Embedding-8B, ~75+ average task score on MTEB and currently #4 on MTEB English; the models above it are research-only, so it's the top usable model). Matryoshka dimensions let you truncate to fewer dims — re-normalized — for cheaper vectors. Requires a FORGE_API_KEY (free tier at dash.voxell.ai). Embeddings only — no data is stored.

Launch a hosted Gemini File Search true multimodal RAG layer that answers from your PDFs, docs, screenshots, charts, diagrams, help center, FAQs, and images with citations people can verify.

Persistent memory for any AI assistant — Claude, ChatGPT, Cursor — backed by your local Obsidian vault. One daemon, all your tools.

Self-hosted semantic memory layer for Claude and MCP-compatible AI clients. Store notes, search by meaning not keywords, and recall relevant context automatically. Built on Cloudflare Workers, D1, Vectorize, and Workers AI. Runs on the free tier. One-click deploy.

Audit-grade memory backbone for agent teams. Bi-temporal facts (event time + transaction time, with recall(as_of=...) replay), 6-step deterministic retrieval (no LLM in the critical path), conversation ingest with speaker-locked dual-pass extraction, per-tenant Postgres row-level security, and Ed25519-signed provenance. Postgres + pgvector + Neo4j defaults; ArangoDB / DynamoDB / Cosmos / AlloyDB also supported. Listed on the official MCP Registry as io.github.bolnet/attestor.

Your Data. Your AI. Your Rules. Ontheia is a self-hosted, open-source AI agent platform. Run AI agents, automate workflows, and connect AI models to any tool — entirely on your own infrastructure, without sending data to external cloud services.

Strata is a self hosted AI memory server. Your AI remembers everything across every session, on your own hardware. Key features: . Semantic search (find memories by meaning, not keywords) . Per-agent API keys with granular permissions . 3D constellation viewer with live agent activity . File vault (attach real documents to memories) . CSV audit log (full transparency on every agent action) . Pre-Strata history import (your memory doesn't start at install day) . Global MCP kill switch (emergency brake, only a human can undo) . Automatic deduplication . 10 structured thought types . Backend using PostgreSQL . Runs on a Raspberry Pi Always on, always local, always yours.

x402 micropayment API for AI agents. In-memory vector store with cosine similarity search. For RAG pipelines. Pay per call with USDC on Base.

Persistent semantic memory for AI tools. Supabase + pgvector + Deno Edge Function. Stores decisions, insights, and project context with auto-extracted topics. Search by meaning, not keywords. Works with Claude Code, Cursor, Windsurf, ChatGPT, and any MCP client. Deploy in 10 minutes.

Persistent project memory for AI coding agents. Hybrid vector + BM25 search with AST-aware chunking (24 languages), dependency graph boosting, conversation memory, checkpoints, annotations, and wiki generation. Local-first, zero config. `bunx mimirs init`

Persistent memory for any AI assistant. Zero token cost until recall. Stores memories locally in SQLite, ranks by 6-factor scoring, returns results in TOON compact format (79% smaller than JSON). 17 MCP tools, 20 HTTP endpoints, 25 CLI commands. 4 tiers from keyword to autonomous with local LLMs via Ollama. 97.8% recall accuracy on ICLR 2025 LongMemEval benchmark. Works with Claude, ChatGPT, Grok, Cursor, Windsurf, Continue.dev, OpenClaw, Llama, and any MCP client. Open source, MIT licensed, written in Rust.

Persistent shared memory for AI agents. Hybrid search (pgvector + tsvector), knowledge graph, cognitive scoring, and 16-language temporal extraction. 97.2% Recall@10 on LongMemEval with one PostgreSQL query. Works across Claude Code, Cursor, Codex, OpenClaw, and any MCP client

Persistent shared memory for AI agents. Hybrid search (pgvector + tsvector), knowledge graph, cognitive scoring, and 16-language temporal extraction. 97.2% Recall@10 on LongMemEval with one PostgreSQL query. Works across Claude Code, Cursor, Codex, OpenClaw, and any MCP client

A powerful Model Context Protocol (MCP) server using gemini embedding 3 that transforms any local directory into an ultrafast, visually-aware spatial search engine for AI agents.

Transform 17 source types (docs, GitHub repos, PDFs, videos, Jupyter, Confluence, Notion, Slack/Discord) into AI-ready skills and RAG knowledge. 35 MCP tools for scraping, packaging, and exporting to vector databases. Supports 16+ LLM platforms.

One-line MCP config gives your AI agent persistent memory with automatic workflow rules. Unlike passive memory stores, Awareness teaches your agent WHEN to remember and recall — works across Claude Code, Cursor, Windsurf, Cline, Copilot and 7 more IDEs.

MCP server for Manticore Search — enables AI assistants to query and manage Manticore Search databases directly. Supports full-text search, KNN vector search, fuzzy search, and real-time data access.

AI-Ready Data & Context Engineering API. Connect any data source — PostgreSQL, CRMs, APIs — and get clean, structured, AI-ready data in seconds. Natural language queries, semantic vector search, automated PII redaction, deduplication, and AI-powered context building for RAG pipelines. 25 free credits on signup.

MCP server with 20 tools for web design analysis — layout extraction, motion detection, quality scoring, and semantic search via Playwright, pgvector, and Ollama.

MCP persistent memory service that gives AI assistants long-term memory across sessions and projects. Powered by Qdrant vector search + Ollama/Gemini embedding with hybrid retrieval (vector + BM25). Features include: semantic memory save/search/forget, content deduplication via SHA-256, automatic sensitive data redaction, dual-shell architecture (MCP stdio + HTTP REST), Web Admin Panel with analytics, multi-user auth & API key management, and audit logging.

Enterprise-grade (40m+ lines) codebase intelligence in a zero-setup, private and local 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.

Hierarchical memory for AI agents. Three-layer (buffer/working/core) with automatic decay, promotion, and semantic search.

Memory with integrity for AI coding agents. SAME tracks provenance, flags stale knowledge, and surfaces contradictions; so your AI trusts what's current, not what's outdated. 17 MCP tools: semantic search, cross-vault federation, session handoffs, decision logging, knowledge graph, trust-aware retrieval, consolidation, health analysis. Provenance on every write. Stale notes rank lower automatically. SQLite + vector search. Ollama, OpenAI, LM Studio, or keyword-only. One 12MB Go binary. No cloud, no API keys, no telemetry. Your notes never leave your machine. Claude Code, Cursor, Windsurf, Codex CLI, Gemini CLI, and any MCP client.

MCP server for Voyage AI embeddings, reranking, and MongoDB Atlas Vector Search. Provides 11 tools for semantic search, document ingestion, cost estimation, and model exploration. Full RAG pipeline: chunk, embed, vector search, and rerank from any MCP client.

Persistent semantic memory for Claude Code, chats AND code. 74+ MCP tools, local pgvector embeddings, codebase indexing with 14 language extractors, multi-agent team coordination. Self-hosted, zero cloud dependency.

Stop re-explaining yourself to Claude every session. Ace Memory gives Claude Code persistent memory across sessions with 21 tools: save and search memories by meaning (not just keywords), auto-log git commits, capture ideas silently and resurface them when relevant, preserve discussion threads through context compression, and track full session history. Smart memory lifecycle auto-decays unused memories while protecting your preferences and corrections forever. Cloud-native storage via Supabase + pgvector means your memories survive machine wipes and work across devices. Setup takes 5 minutes.

A Model Context Protocol (MCP) server that exposes GeoServer REST API functionality for natural language interaction. Built on the python-geoservercloud library, this MCP server provides 80+ tools for managing: - Workspaces - create, list, delete workspaces - Datastores - connect to PostGIS, GeoPackage, Shapefile sources - Feature Types & Layers - publish and configure vector/raster layers - Styles - create and apply SLD/CSS/MBStyle styles - Layer Groups - organize layers into groups - Security - manage access rules and permissions Works with any GeoServer instance (local or remote). Simply configure your GeoServer URL and credentials via environment variables.

A high-performance, pure Rust Model Context Protocol (MCP) server that provides persistent, semantic, and graph-based memory for AI agents. Unlike other memory solutions that require a complex stack (Python + Vector DB + Graph DB), this project is a single, self-contained executable. ✅ No External Database (SurrealDB is embedded) ✅ No Python Dependencies (Embedding models run via embedded ONNX runtime) ✅ No API Keys Required (All models run locally on CPU) ✅ Zero Setup (Just run one Docker container or binary) It combines: Vector Search (FastEmbed) for semantic similarity. Knowledge Graph (PetGraph) for entity relationships. Code Indexing for understanding your codebase. Hybrid Retrieval (Reciprocal Rank Fusion) for best results.

Vector Memory MCP Server A secure, vector-based memory server for Claude Desktop using sqlite-vec and sentence-transformers. This MCP server provides persistent semantic memory capabilities that enhance AI coding assistants by remembering and retrieving relevant coding experiences, solutions, and knowledge. ✨ Features 🔍 Semantic Search: Vector-based similarity search using 384-dimensional embeddings 💾 Persistent Storage: SQLite database with vector indexing via sqlite-vec 🏷️ Smart Organization: Categories and tags for better memory organization 🔒 Security First: Input validation, path sanitization, and resource limits ⚡ High Performance: Fast embedding generation with sentence-transformers 🧹 Auto-Cleanup: Intelligent memory management and cleanup tools 📊 Rich Statistics: Comprehensive memory database analytics 🔄 Automatic Deduplication: SHA-256 content hashing prevents storing duplicate memories 📈 Access Tracking: Monitors memory usage with access counts and timestamps for optimization 🧠 Smart Cleanup Algorithm: Prioritizes memory retention based on recency, access patterns, and importance

About Model Context Protocol server that packages GDAL-style geospatial workflows through Python-native libraries (Rasterio, GeoPandas, PyProj, etc.) to give AI agents catalog discovery, metadata intelligence, and raster/vector processing with built-in reasoning guidance and reference resources.

**Context Keeper** 基于LLM驱动的智能上下文记忆管理系统，专为AI Agent提供企业级记忆能力。 **核心创新**：独创"宽召回+精排序"架构，通过两阶段LLM协作实现多维度检索融合（向量+时间线+知识图谱），准确率从传统RAG的45%提升至**75%+**，召回率达**80%+**。 **技术亮点**：完整实现MCP协议（HTTP/WebSocket/SSE三种传输），四维统一上下文模型（Project/Topic/Conversation/Code），支持短期/长期记忆智能转换。 **性能表现**：10000+ QPS并发能力，P95延迟<500ms，用户/工作空间完全隔离。 **开源生态**：MIT许可，Go语言实现，深度集成Cursor/VSCode IDE。让AI助手真正拥有持续记忆能力，告别重复解释，开启智能协作新时代。

One MCP to Rule Them All. NCP consolidates over 50 scattered MCP tools into two unified tools (Find & Run). Your AI goes from confused to confident with 87% fewer tokens, zero tool confusion, and 5x faster responses. Perfect for teams drowning in MCP tool sprawl.

Transform your AI agents into Apple development experts! Apple RAG MCP gives you instant access to official Swift docs, design guidelines, and comprehensive Apple platform knowledge through cutting-edge RAG technology. With professional AI reranking and hybrid search across iOS, macOS, watchOS, tvOS, and visionOS documentation plus Apple Developer YouTube content, you'll get precise, contextual answers every time. Compatible with Cursor, Claude Desktop, and all MCP tools - start building smarter Apple apps today!

A Model Context Protocol (MCP) server that leverages a vector database to efficiently index and query the codebase.

Claude Context is an MCP plugin that adds semantic code search to Claude Code and other AI coding agents, giving them deep context from your entire codebase.

Spring AI Playground is a self-hosted web UI that simplifies AI experimentation and testing. It provides Java developers with an intuitive interface for working with large language models (LLMs), vector databases, prompt engineering, and Model Context Protocol (MCP) integrations. Built on Spring AI, it supports leading model providers and includes comprehensive tools for testing retrieval-augmented generation (RAG) workflows and MCP integrations. The goal is to make AI more accessible to developers, helping them quickly prototype Spring AI-based applications with enhanced contextual awareness and external tool capabilities.

An MCP server for Milvus that supports session connection management and accesses Milvus via the SSE interface.

Model–Context–Protocol (MCP) Server A modular, extensible Rust-based server providing short-term, long-term, and latent memory services, a chat endpoint backed by a BaseAgent + Sentience DSL, and seamless integration with ChromaDB and LLM services.

A powerful MCP server for generating, editing, and converting SVG images using AI, uses the best vector generation models

A Model Context Protocol (MCP) server for agentic retrieval and semantic search over unstructured and structured data using Milvus, a high-performance vector database. This server enables large language model (LLM) applications to efficiently index, store, and retrieve vector embeddings derived from diverse data sources—such as documents, images, metadata, or logs. By leveraging Milvus, the MCP server supports similarity search and contextual retrieval, enabling intelligent access to relevant information based on natural language queries. Designed for integration with MCP-compatible clients, this solution provides a scalable, low-latency foundation for building AI-powered applications with contextual awareness and retrieval-augmented generation (RAG) capabilities.

Self-hosted MCP Japanese text indexing & search—chunking+embeddings with BM25×vector rerank

API service to search vectorized Cursor IDE chat history using LanceDB and Ollama

API service to search vectorized Cursor IDE chat history using LanceDB and Ollama

Model Context Protocol Server for Mochow

MCP server for MariaDB

Servidor MCP que busca documentação mais atualizada de tools

🍀 🤖 Spring Boot AI - Evaluation Testing

A MCP (Model Context Protocol) server providing long-term memory for LLMs

A Model Context Protocol (MCP) server that enables AI assistants to interact with HubSpot CRM data, providing built-in vector storage and caching mechanisms help overcome HubSpot API limitations while improving response times.

OAuth2 Authorization Server with Spring Boot 3 and Java 24

A demo repository for testing the GitHub MCP server

A powerful knowledge management system that forges wisdom from experiences, insights, and best practices. Built with Qdrant vector database for efficient knowledge storage and retrieval.

The Cursor10x MCP is a persistent multi-dimensional memory system for Cursor that enhances AI assistants with conversation context, project history, and code relationships across sessions.

Learn TypeScript chatting effortlessly with AI

MCP Server that integrates a knowledge graph and a vector database based on SQLite

This is a MCP server I built to interact with my hybrid graph rag db.

💬 MaxKB is an open-source AI assistant for enterprise. It seamlessly integrates RAG pipelines, supports robust workflows, and provides MCP tool-use capabilities.

YouTube MCP Server is an AI-powered solution designed to revolutionize your YouTube experience. It empowers users to search for YouTube videos, retrieve detailed transcripts, and perform semantic searches over video content—all without relying on the official API. By integrating with a vector database, this server streamlines content discovery.

A Model Context Protocol (MCP) server implementation for RAG

LanceDB MCP Server nodejs

🗂️ A Model Context Protocol (MCP) server that provides integration with Turso databases for LLMs. This server implements a two-level authentication system to handle both organization-level and database-level operations, making it easy to manage and query Turso databases directly from LLMs.

🗂️ A Model Context Protocol (MCP) server that provides integration with Turso databases for LLMs. This server implements a two-level authentication system to handle both organization-level and database-level operations, making it easy to manage and query Turso databases directly from LLMs.

[C#/AI] (LibraryAI/ConsoleAI) AI 知识库与数据处理程序

Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.

An MCP server that provides tools for retrieving and processing documentation through vector search, both locally or hosted. Enabling AI assistants to augment their responses with relevant documentation context.

A Model Context Protocol (MCP) server implementation that provides vector database capabilities through Chroma.

Model Context Protocol (MCP) server implementation for semantic vector search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic vector database search capabilities. You can use Claude and Cline AI as well.

MCP server providing semantic memory and persistent storage capabilities for Claude using ChromaDB and sentence transformers.

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.