"Information about memory" matching MCP servers:

Local, searchable project memory for AI coding agents. Markdown source of truth, MCP interface, safe structured updates

A Linux system monitoring MCP server that provides real-time information on CPU, memory, disk, network, processes, Docker, security, and more via MCP tools.

Manages an agent's long-term memories as versioned markdown files with searchable SQLite indexes, supporting lifecycle states, conflict-gated recall, revision, and explicit forgetting without automatic deletion.

I gave Claude a mass grave of 200 markdown files and now it remembers my entire project between sessions. No cloud, no API keys, one 10mb Go binary, and private. Stateless Agent Memory Engine is your synapse to the CLI.

Failure Memory provides AI coding agents with a shared local memory of failures, enabling them to record, recall, and learn from mistakes across sessions.

Enables users to interact with container registries through the ORAS CLI, providing information about container images, platforms, and signatures via natural language queries.

Give your coding agent the dependency graph it is about to change: scan, diff, explain, audit.

Persistent project memory and task management for AI coding agents through MCP. Lets Claude Code, Cursor, Codex and other MCP clients save decisions and bug fixes, search relevant context, and reuse it across sessions.

Enables LLM agents to maintain structured long-term memory and knowledge using SQLite, with graph storage and relational query tools for persistent context.

Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.

🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.

A memory, docs, and repo context layer for engineering agents, providing typed persistent memory, RAG document retrieval, and repository-aware tools to recall decisions, runbooks, and operational knowledge across sessions.

About Self-hosted AI agent memory server with MCP, evidence provenance, typed claims, conflict detection, embeddings, recall, PostgreSQL, and Neo4j.

Minimal long-term memory MCP server for AI assistants backed by SQLite FTS5 full-text search, optional hybrid vector retrieval, scoped memory graphs, and VSCode extension.

Local-first, file-based memory layer for AI agents — one shared Markdown vault across Claude, Codex, Gemini, Cursor and any MCP client. Provides read/write memory tools with an audit trail, per-agent trust levels, and Git sync; no cloud and no lock-in.

Governed multi-agent memory for AI agents. Hybrid markdown + SQLite store with full-text search, vector retrieval, and LLM reranking. Three transports: MCP stdio, HTTP JSON-RPC, and MCP SSE. One Go binary

A read-only MCP server that exposes a self-hosted Atlassian Bamboo instance to AI assistants, enabling natural language queries about builds, deployments, and plan status.

Gives AI coding agents a persistent memory that carries across sessions and projects, letting them save and search decisions, bug fixes, and discoveries through MCP tools. Each new session loads the recent memories of the current project so agents resume with the same context instead of re-explaining it.

Provides coding agents with persistent local memory by exposing tools to save, search, and retrieve decisions, bugs, and context as Markdown with hybrid keyword and semantic search.

An MCP server that provides persistent memory for AI agents, enabling context retention across sessions and supporting vibe-loop workflows where agent promises and deliverables are tracked and validated.

Enables persistent memory for Claude Desktop, allowing Claude to remember and recall information across conversations using PostgreSQL and OpenAI embeddings.

CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.

ContextLattice is an HTTP-first, MCP-compatible memory/context/task orchestrator that persists writes and returns fused recall from specialized stores with local-first defaults. Primary URL: https://contextlattice.io/ Install: https://contextlattice.io/installation.html Troubleshooting: https://contextlattice.io/troubleshooting.html

An MCP server that gives AI agents persistent, narrative memory: concepts as nodes, relationships as typed "because" edges, so anything filed can be pulled back by association rather than by address.

Indexes the session histories Claude Code, Codex CLI and opencode already write into a local searchable memory. MCP recall tools, session-start auto-recall, secret redaction at index time, cross-machine sync over SSH.

Scans source code for possible Korean compliance risks (PIPA, Network Act, Credit Information Act, E-Commerce Act) and maps findings to specific legal provisions. Runs locally, exposes MCP tools for scanning and law lookup, and does not constitute legal advice. That's hedged (matches the project's "possible risk / not legal advice" rule), keyword-rich for search, and names all four laws.

Agent memory for facts that change. When a user corrects something, Recall replaces the old fact, tells the agent what it replaced, and keeps the old value in a history. Ask what was believed on any date, see every change to a fact, or diff two dates. Runs locally on polign_db, Postgres with pgvector, MongoDB, or Qdrant. Apache 2.0.

A local AST and type-aware Go repository context indexer that uses CHA to map symbols, call graphs, dependencies, and architecture patterns. Built for AI coding agents that need to navigate and reason about large Go codebases without reading raw files.

Provides persistent, searchable memory across AI coding agent and chat history (Claude Code, Codex, Gemini CLI, ChatGPT, and more) via retrieval-augmented generation, enabling semantic and hybrid search to retain context across sessions.

Lets AI agents read what Kubernetes containers were doing right before they were OOMKilled or crashed: Java heap histograms and Go heap profiles taken before the kill, logs of the dead instance, and core dumps matched to the pod. Read-only tools to list captures, read summaries and raw evidence, and see which containers are near their memory limit now.