"Trending MCP server for memory usage with GitHub Copilot integration" matching MCP servers:

AI coding usage analytics — track spend, model usage, and costs across Cursor and Claude Code sessions.

Get hot trending topics from social media platforms like Weibo, Douyin, Bilibili, etc. via Streamable HTTP endpoint.

Monitors your Cursor usage and provides a conserve flag to the agent, helping you manage request quota by batching questions, preferring defaults, and cutting unnecessary confirmations.

MCP server that wraps Cursor Enterprise API to enable AI agents to answer questions about team spending, usage, and model adoption.

Enables local-first tracking of AI token usage and costs from Claude Code and OpenCode, answering queries about tokens, models, sessions, and cost through MCP tools and a CLI, with no network calls.

Search and read locally-recorded meeting transcripts over stdio. Read-only, no network. Exposes list_sessions, get_session, and search_transcripts to any MCP client.

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

Hand a project over — to your next session, to Cursor/Codex/Gemini, to your successor — with the reasons attached. Local-first memory MCP for coding agents: decisions re-injected before the agent acts, drift detection across every project you run. One SQLite file, nothing leaves your machine.

Hosted memory for AI agents that learns from outcomes, with shared rooms. One key across Claude, Cursor & ChatGPT.

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 for diagnosing Apollo InMemoryCache normalization defects, providing cache analysis and repair planning tools for React Native apps.

Long-term memory for AI coding agents, stored as a tree of plain local files. Agents search it before answering and save what lasts; changed facts are kept as history and flagged as outdated.

Provides persistent local memory for any MCP-compatible AI agent, enabling agents to store, search, recall, forget, export, and manage preferences, rules, and facts with zero dependencies, no cloud, and no API keys.

A different approach from typical persistent-memory MCPs. Instead of a local SQLite + embeddings store, the memory lives as plain files in a .ai-memory/ directory you commit to your repo (facts.jsonl, decisions/\*.md, gotchas.md). Git is the sync layer — what one Claude/Cursor/Cline learns about a repo, the next session (or a teammate's agent) picks up automatically. 5 MCP tools: get_rep

Enables AI agents to analyze public GitHub repositories directly through tools for repo health scoring, recent commit summaries, good first issue discovery, and semantic Q&A over docs and issues.

MARM MCP provides persistent memory and structured session context beneath any AI tool, so your agents learn, remember, and collaborate across all your workflows.

Enables autonomous AI agents and developers to interact with the GitHub REST API for repositories, pull requests, issues, actions, releases, tags, and user/org data through MCP tools. It uses token-efficient response summaries and optional 3-tier read-only safeguards to keep interactions safe and context-window friendly.

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.

Local-first persistent memory MCP: shared SQLite key/value store, searchable, TTL-aware and secret-safe.

MCP server that wraps GitHub REST API to allow AI agents to search repositories, get repository details, list issues, and read READMEs.