"Information on memory banks or task managers" matching MCP servers:

Async MCP server for running long-running AI tasks with real-time progress monitoring, enabling users to start, monitor, and manage complex AI workflows across multiple models.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

Provides Claude Co-work with MCP tools to search and record organizational memory, including search, read, query, ingest, remember, sync, and status operations.

MCP server providing durable Substrate organizational memory tools, including search, read, query, ingest, remember, and sync. It integrates with Codex to automatically capture completed turns and session boundaries for persistent memory.

Improves AI agent task performance by providing task area segmentation, hierarchical RAG, and hybrid search capabilities.

Exposes task management (add, list, complete tasks) and document search (RAG) as MCP tools for AI agents.

A comprehensive Model Context Protocol (MCP) server implementing the latest MCP specification with tools, resources, prompts, and enhanced sampling capabilities that features HackerNews and GitHub API integrations for AI-powered analysis.

A local MCP server that gives AI assistants persistent long-term memory using a biological neural architecture with cortex, synapses, and hippocampus.

Self-hosted personal knowledge graph for Claude that persists across sessions, devices, and tools. Built on Neo4j with local semantic embeddings; OAuth 2.1 lets Claude Code, Claude Desktop, and claude.ai web all hit the same graph.

Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.

MCP server providing persistent memory for AI applications, enabling Claude to store and recall memories with hybrid search and knowledge graph.

Provides persistent memory for AI agents using hybrid search (vector embeddings + BM25) with neural reranking, enabling storage and retrieval of insights, debugging solutions, and patterns across coding sessions.

Provides AI agents with persistent, searchable memory using semantic search, auto-linking, and categorization, with zero-config local setup or production-ready external providers.

An MCP server providing long-term memory for LLMs via hybrid search (FTS5 + vector) stored in SQLite, with time-decay scoring and support for multiple AI assistants.

Enables agents to manage persistent long-term memory through MCP tools for searching, adding, updating, giving feedback, and forgetting memories, with automated pipelines for redaction, distillation, and reconciliation.

Cognitive memory engine for AI agents with 5,100+ knowledge modules, circadian rhythm awareness, emotional state tracking (PAD model), and hybrid semantic search. Supports persistent per-user memory, project-scoped contexts, and multi-protocol access.

A stateless MCP server that gives coding agents a curated entry point to shared memory, orchestrating Hindsight (facts) and LightRAG (corpora) for automatic routing, URL ingestion, and consistency tooling.

MCP server for persistent, semantic memory across AI sessions; store context, decisions, and learnings and recall them with natural language search.

Local-first agentic knowledge layer over Obsidian notes, enabling MCP-aware agents to search, retrieve, and compile knowledge with provenance and task contracts.

Provides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.