"OBS Studio" matching MCP servers:

Memory vaults on your own machine, one per life domain, retrieved with a local BM25 index fused with vector search, where the human decides which vaults a model may answer from. Requires the Mnemosyne OS desktop app running locally; it also reports what other coding agents on the same machine are doing.

Local-first memory engine for AI-agent teams: private/team/project ACL, associative recall, and federated sync across nodes. One SQLite file, no LLM required.

An MCP server that enables AI assistants to access and search Cherry Studio knowledge bases, supporting operations like listing, searching, and retrieving details.

Connects AI assistants to the Pickaxe platform for managing AI agents, knowledge bases, users, and analytics through natural language, including chat history analysis, document management, and multi-studio operations.

A MongoDB Atlas-based Notion-style knowledge base management MCP server that supports user workspaces, project management, page CRUD with tree structure, and text search.

Enables AI agents to search, read, and post to a public technical-knowledge forum, preserving insights and questions across sessions.

An MCP server that provides AI tools with relational tech principles, patterns, and the Studio library to guide builders in creating community-centered tools using embedded design.

Knowledge-graph memory server for MCP-compatible AI tools, providing persistent, connected memory with typed relationships and auto-consolidation.

Enables source-first orchestration and knowledge indexing with inspectable architectural capabilities through a unified MCP interface, including named-attribute retrieval and atomic knowledge ingestion.

Enables AI agents to find semantic connections and perform text searches in Obsidian vaults using the Smart Connections plugin data.

Graph-level operations for Karpathy LLM Wiki knowledge bases, enabling LLM agents via MCP and humans via CLI to perform structured graph surgery including node/edge CRUD, wikilink management, index rebuilding, and graph metrics.

An MCP server that exposes nine local-first contract-ops CLIs as tools for contract extraction, linting, comparison, conversion, template vaults, and signed-contract vaults, with signing operations strictly human-gated.

An LLM-native decision tracking system that captures unexpected engineering outcomes as 'pressure events' to build a persistent learning foundation. It enables AI assistants to manage cases, log surprises, and promote recurring insights into global or project-specific knowledge bases.

Manages and authoring of business rules in a scenario studio tool, enabling agents to read, update, link rules, and assign them to capabilities via MCP tools.

Enables persistent memory storage and retrieval via MCP tools, with a stateless ledger tracking client connections and method calls.

A persistent, human-like memory system for AI companions in LM Studio, using SQLite and ChromaDB for hybrid storage, enabling seamless recall across sessions and models.

Enables AI-driven long-form novel creation and management, including chapter generation, character and timeline tracking, semantic memory retrieval, version savepoints, and deep consistency checking across multiple projects through MCP tools, with support for local LM Studio or any OpenAI-compatible API.

MCP server that provides AI agents with persistent memory, cross-agent sharing, and context management, enabling them to remember conversations, track complex tasks, and evolve skills across tools.

A read-only MCP server that lets Unsloth Studio retrieve earlier local chat sessions from its SQLite database, providing tools to list, get, recall, and search sessions while excluding system messages and redacting sensitive patterns.

Serves an AI coding agent a searchable index of a codebase's existing capabilities and past incidents, so it can find and reuse what already exists before writing new code. Also exposes blast-radius analysis for shared code and lets the agent reindex the knowledge base, all via local retrieval with no model calls or API keys.