"Connecting to a vector knowledge base" matching MCP servers:

A knowledge base MCP server that aggregates team knowledge from multiple sources into Postgres. It provides hybrid search (full-text + vector + RRF) via MCP tools, and enables direct recording of decisions, learnings, and pitfalls.

Exposes a knowledge base to LLM clients through tools, resources, and prompts.

A cross-project knowledge base MCP server supporting three-layer search (text matching, TF-IDF, and semantic vectors) with a Web UI for management.

Enables local full-text and semantic search over saved content, providing search and retrieval capabilities via MCP for AI assistants.

Provides semantic search over a self-building personal technical knowledge base that automatically collects and distills content from GitHub Trending, AI news, and arXiv.

Provides semantic search and data retrieval capabilities over a knowledge base with multiple tools including keyword search, category filtering, and ID-based lookup with in-memory caching.

Provides cited retrieval over local-first knowledge sources (TXT, CSV, Google Sheets, Zoho Mail, MBOX) via SQLite FTS5, with tools for searching and checking source status, returning evidence links instead of generated answers.

Gives AI agents persistent, shared memory by ingesting notes, code, transcripts, and session captures, then serving token-optimized context and hybrid keyword/semantic search over MCP and REST. Agents can classify, promote, and synthesize stored material so each session builds on prior work instead of starting from zero.

Enables AI agents to share persistent memory by ingesting sources, classifying and synthesizing knowledge, and retrieving token-optimized context through MCP and REST APIs.

Enables AI agents to share persistent, searchable memory across sessions by ingesting and organizing knowledge from sources like Obsidian, code, transcripts, and terminal sessions. It provides token-optimized retrieval, semantic and keyword search, and MCP/REST access so agents can capture, classify, synthesize, and reuse context.

Enables AI agents to capture, classify, synthesize, and retrieve shared persistent knowledge across sessions via MCP, with tiered memory, full-text and semantic search, and token-efficient context briefings.

A persistent memory system that captures, classifies, synthesizes, and retrieves knowledge for AI agents, turning raw information into refined, retrievable knowledge that compounds intelligence over time.

A local knowledge base server that connects to AI assistants, turning markdown files into a semantically searchable memory layer via OpenAI embeddings and SQLite.

Serves AI agents a shared persistent memory that ingests Obsidian vaults, code, transcripts, bookmarks, and session or bug-fix captures, then classifies, promotes, and synthesizes them into tiered, retrievable knowledge. Exposes token-optimized context briefings and hybrid keyword/semantic search over MCP so multiple agents can read and write the same brain and improve across sessions.

Enables AI agents to share persistent memory by ingesting knowledge from sources like Obsidian, code, documents, web clips, and terminal sessions, then providing token-optimized search, context, classification, synthesis, and capture tools through MCP and REST APIs.

Enables AI agents to share a persistent, self-improving memory by ingesting sources such as Obsidian vaults, code, transcripts, and bookmarks, then classifying, promoting, synthesizing, and retrieving that knowledge through token-optimized search across MCP and REST interfaces. Agents can capture sessions and fixes so context compounds across sessions and across Claude, Codex, Gemini, and any other MCP client.

Enables AI agents to share persistent, searchable memory by capturing, classifying, synthesizing, and retrieving knowledge from sources like Obsidian, code, transcripts, bookmarks, and sessions via MCP and REST with token-optimized context.

Enables AI agents to capture, classify, synthesize, and retrieve persistent knowledge through MCP and REST APIs, so multiple agents can share memory and improve across sessions. It provides full-text, semantic, and hybrid search with token-optimized context retrieval from sources like Obsidian, code, transcripts, and terminal sessions.

Enables AI agents to share a persistent memory that captures, classifies, synthesizes, and retrieves information through token-optimized tiered search combining full-text and semantic queries. Multiple agents read from and write to the same store, so each session compounds intelligence over time.

Enables AI agents to share persistent, searchable memory by ingesting and syncing knowledge from sources like Obsidian, code, documents, and web captures, then serving it through MCP and REST APIs with classification, synthesis, and tiered retrieval.