#vector-search

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 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.

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.

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 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.

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.

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.

**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!

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.

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

LanceDB MCP Server nodejs

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.