"GitHub repository for litopys-dev/litopys project" matching MCP servers:

Persistent graph-based memory for AI agents, stored as plain markdown — no vector DB. Typed nodes and 11 relation types via 5 MCP tools (search, get, create, link, related), stdio and HTTP/SSE transports.

MCP server that provides a citation-first memory layer for AI agents, enabling verified search across Git-backed repositories and optional conversation memory. It exposes tools for doctor, sync, search, get, init, and ingest, returning JSON results with verified citations or abstain status.

Enables AI agents to search, read, and traverse a local knowledge base of Markdown files using full-text search and relationship graph, reducing token usage.

Provides tools for managing project knowledge graphs, enabling structured representation of projects, tasks, milestones, resources, and team members.

Local workspace memory for Claude Desktop. Indexes documents into a vector store with hybrid search, cross-session memory, auto-learn, and knowledge graph.

Persistent AI knowledge base for Claude via Model Context Protocol — fully offline, no cloud API key required. Stores architectural decisions, technical debt, project progress, and domain knowledge in versioned Markdown files.

A focused project memory system that provides knowledge graph capabilities and SQLite database operations for intelligent entity tracking and workflow management. It enables AI models to maintain persistent project context through 17 specialized tools, 10 resources, and 27 pre-built prompts.

Provides a file-based, git-friendly memory store for coding agents to persist and recall project-specific lessons, bugs, and conventions via JSON files in the repository.

A self-hosted remote MCP server for cross-model AI session handoff, allowing users to start a project in one AI assistant and continue in another via shared project logs, files with version history, and full-text search, all stored in a single SQLite file on their own server.

An MCP server that transforms text into knowledge graphs and autonomously generates insights by combining Montague Grammar with Zettelkasten methodology.

Local repository memory for Codex that indexes TypeScript/JavaScript structure into local SQLite, exposes compact MCP tools, and renders deterministic SVG/map frames.

An intelligent personal CRM that processes WhatsApp conversations to build a searchable knowledge base about contacts using diarization, transcription, and PII sanitization. It exposes MCP tools for semantic search, contact summaries, and reminder management within Claude Desktop.

A bridge between MCP Host applications and mem0 cloud service, specialized for project management with capabilities to store, retrieve, and search project information within a structured format.

Stores manual project memories, enables semantic retrieval, and tracks per-memory confidence using PostgreSQL with pgvector and OpenAI-compatible embeddings.

A local-first MCP server and CLI that gives coding agents structured project memory, task contracts, context packs, backlog workflows, and verification evidence, storing data in reviewable Markdown/YAML with a fast SQLite index.

Enables coding agents to persist notes, decisions, requirements, and learnings per project in SQLite and retrieve them across sessions via search and status tools.

Provides AI-driven project memory management through structured prompts that help Claude parse tasks from specs, review code changes, sync with commit history, and maintain project documentation without directly accessing files.

Provides Claude Code with cross-session long-term memory via Letta, allowing it to read and store project context, learnings, and decisions.

Local MCP server giving AI coding agents (Claude Code, Cursor, VS Code/JetBrains Copilot) a shared, persistent memory of your projects and every bug/issue faced during development. Stateless, plain-file storage (AGENTS.md + issues.jsonl) — no database.

A cross-project knowledge graph for MCP that maps features across mobile, backend, and admin codebases, giving AI agents full-stack context.