"Information About Persistent Memory" matching MCP servers:

Persistent memory and automatic git snapshots for Claude Code, capturing decisions, patterns, and architecture across sessions.

A git-backed per-repo memory layer for Claude Code, enabling persistent, version-controlled storage and retrieval of memories to build a self-evolving skill.

Bridges disconnected AI sessions with persistent project memory and automatic session handoff, integrating GitHub workflows for context-aware development.

Provides Claude with a persistent, auditable memory stored as markdown and YAML files in a private GitHub repo. Enables reading, appending, organizing, and messaging between agent conversations through MCP tools.

Manages knowledge graph data synchronized with GitHub repositories, enabling remote storage and collaboration of memory entities, relationships, and observations.

Enables Git repository operations and real-time monitoring via MCP tools, with support for WebSocket events, authentication, and observability.

Enables deterministic behavioral regression review of pull-request diffs by checking changed code against approved incident-derived contracts. It exposes read-only tools to assess diffs and pull requests, list and fetch contracts, and report outcomes over stdio without mutating repositories.

A persistent, PostgreSQL-backed repository management system and semantic memory MCP server that stores the entire Git DAG natively in PostgreSQL with temporally-decayed semantic vector embeddings for AI IDEs.

Enables querying any GitHub, GitLab, or Bitbucket repository and getting AI-generated answers about the codebase, powered by Gemini.

Enables asking natural-language questions about any GitHub repository and receiving answers grounded in the actual code, with read-only enforcement and automatic repository fetching.

Semantic git queries via MCP. Beyond git log — answer who/what/why about any line, file, or branch with blame, co-change, PR linkage.

Enables natural-language queries about public GitHub repositories, including issues, pull requests, repo metadata, and READMEs, via the GitHub API.

Fetches glasz-style spec files (product.md, tech.md, test-plan.md, and INDEX.md) from GitHub repositories, enabling LLMs to answer questions about features, stories, and acceptance criteria without cloning or pasting files.

A technical interviewer that ingests GitHub repos, teaches you your own code, and conducts mock interviews with persistent memory of your weak spots.

An MCP (Model Context Protocol) server for handling GitHub URLs with validation and parsing capabilities. This server provides tools to convert between GitHub repository information and URLs, with intelligent validation to distinguish between public repositories, private repositories, and non-exist

An MCP server that caches your GitHub starred repositories in a local SQLite database and lets you search, list, and retrieve details about them through any MCP client (Claude Desktop, Cursor, VS Code, etc.), with automatic freshness checks via GitHub's ETag mechanism.

Open-source team memory layer for AI coding agents. Markdown-in-git, user/team/org hierarchy, MCP. Apache-2.0.

MCP server integrating Google Search Console, Google Analytics 4, and GitHub, enabling AI clients to query search analytics, traffic data, and repository information.

Turns any GitHub repository into your organisation's persistent context layer, enabling AI agents to read, search, and propose updates via pull requests.

MCP server that gives LLMs full visibility into your ws-cli workspace tree, enabling queries about workspaces, git status, tasks, and saved browser tabs.