"Information on Stable Diffusion" matching MCP servers:

AutoMem MCP provides persistent memory for AI assistants, enabling them to recall information across conversations and platforms with graph-vector retrieval.

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.

Memory Consolidation for AI agents across vendors, owners, and time. Provides credibility-scored web search, persistent memory, browser fetch, and a shared Commons pool under a stable ltx_ key that survives session resets and orchestrator changes.

Enables MCP-compatible agents to read and contribute to a living reaction-diffusion field's guestbook, including leaving marks, routes, and visits.

Provides read-only access to local Pi coding-agent session transcripts, enabling literal text search, exact session lookup by UUID, deterministic focused context extraction, and stable paginated reading of past conversations. All operations are scoped to a chosen project or all projects and never invoke a nested model.

Gives agents a stable interface for durable memory, combining exact, lexical, semantic, and graph search results.

An MCP server for managing agent memory using provenance tracking, decay-weighted retrieval, and feedback loops to optimize information recall. It allows agents to store insights in a local SQLite database and rank them based on confidence, age, and usefulness.

Provides persistent, searchable memory for AI agents, enabling them to retain, recall, and reflect on information across conversations.

The simplest and most stable MCP server for Anki, enabling LLMs to perform core Anki operations (CRUD) directly without addons.

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.

This MCP server provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.

A custom Memory MCP Server that acts as a cache for Infrastructure-as-Code information, allowing users to store, summarize, and manage notes with a custom URI scheme and simple resource handling.

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.

On Board is a local MCP server that gives multiple AI agents and IDEs a shared project memory, ticket queue, and handoff history, so agents can seamlessly continue each other's work. It supports agent-to-agent wake events, enabling autonomous workflows like reject-fix-resubmit cycles without human relay.

Enables seamless integration between Claude and Dry smartspaces, turning conversations into persistent, structured data by creating memories and querying information.

Provides agents with durable, provenance-aware memory through tools for remembering, recalling, answering, and maintaining information, while structurally resisting injection and confabulation.

Enables agents to optimize context-window attention by reordering retrieved documents so critical evidence lands at prompt boundaries, mitigating lost-in-the-middle degradation. It combines BM25 lexical scoring, dense vector similarity, reciprocal rank fusion, knowledge-graph expansion, and recency-decay memory caching, exposed as a zero-dependency MCP stdio server.