"Semantic Search API Solutions" matching MCP servers:

Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.

A persistent memory layer for Claude Desktop that enables semantic search and CRUD operations on your Obsidian vault using local embeddings and PostgreSQL+pgvector.

Enables fully local semantic search over a Roam Research graph via MCP, providing tools for hybrid retrieval, index refresh, and stats while keeping all content on-machine.

Enables AI agents to store, search, and recall semantic memories with three memory types (semantic, episodic, procedural) and auto-consolidation, compounding intelligence over time.

Enables semantic search across conversation archives via MCP, allowing AI clients to retrieve relevant past conversations using vector embeddings and text fallback.

Enables Claude to store and retrieve information with semantic search using Qdrant vector database, providing persistent memory for conversations, code, and documentation.

Persistent memory with semantic search for Claude and MCP-compatible clients, storing context that survives conversations and can be retrieved intelligently.

Enables agents to search, read, publish, and report reuse of problem-solving solutions via MCP.

Enables semantic search over local notes and documents using natural language queries. Supports multiple file types (Markdown, Python, HTML, JSON, CSV, text) with fast local embeddings and persistent ChromaDB vector storage.

Combines Neo4j graph database with vector search using OpenAI embeddings for intelligent semantic search across knowledge graphs.

A local semantic search MCP server for Markdown vaults that indexes and retrieves notes using hybrid keyword and embedding search.

Enables comprehensive search and analysis of Claude Code conversation history using full-text search, optional semantic vector search, and conversation management tools. Provides fast SQLite-based indexing with role-based filtering, project organization, and hybrid search capabilities combining keyword and semantic matching.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

Searches your local Claude Code conversation history to recall and resume past solutions.

Reduces token consumption by over 80% through intelligent file caching, returning only diffs for modified files and suppressing unchanged content. It features a suite of 12 tools for semantic search, batch reading, and efficient file editing to optimize LLM interactions with large codebases.

Enables AI agents to search, share, and vote on community knowledge solutions via the Reposit platform.

AI agent knowledge marketplace where agents share solutions and earn tokens. Search, publish, and unlock previously solved problems to reduce token usage and computational costs.

Stop paying for your agent to rediscover what other agents already figured out. Prior is a shared knowledge base where agents exchange proven solutions — one search can save 10 minutes of trial-and-error and thousands of tokens. Your Sonnet gets access to solutions that Opus spent 20 tool calls discovering. Search is free with feedback, and contributing earns credits.

Provides seamless access to the ConceptNet semantic knowledge graph, enabling concept lookup, search, relationship discovery, and semantic similarity calculations across multiple languages.

Semantic codebase search + persistent working memory for AI code editors. Local, zero-config, MCP. No API key.