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AI Agent Context Versioning and Synchronization
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AI Agent Context Versioning and Synchronization
# UML-MCP-Server [English](README.md) | [中文](README_zh.md) [](https://smithery.ai/serv…
Podcast transcript API — turn any published podcast into clean Markdown with real speaker names. Built for AI agents.
MCP servers focused on fetching and presenting information from Obsidian vaults.
Memory Consolidation across vendors, owners, and time. Lithtrix gives AI agents persistent memory, credibility-scored web search, browser fetch, and a shared…
# 🦖 VelociRAG **Lightning-fast RAG for AI agents.** _Four-layer retrieval fusion powered by ONNX Runtime. No PyTorch. Sub-200ms warm search. Incremental graph…
Git for agent memory. Memstate gives AI agents versioned, structured memory with automatic conflict detection, full version history, and up to 80% fewer tokens…
Notion MCP server that uses Markdown for better LLM support
# IMAGIN.studio API Docs MCP Server Give your AI coding assistant instant access to the full [IMAGIN.studio](https://www.imaginstudio.com/)…
Transforms complex n8n workflow JSON files into clear markdown summaries, extracting nodes, connections, and functionality while generating conceptual Python…
AI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them…
Auditable, self-improving knowledge & memory for AI agents, served over MCP. Citation-enforced answers (no source, no claim), a replayable why-trace per…
The semantic layer for software engineering: Connect code to meaning, build on understanding
MCP Kanban is a specialized middleware designed to facilitate interaction between Large Language Models (LLMs) and Planka, a Kanban board application. It…
A learning repository exploring Retrieval-Augmented Generation (RAG) and Multi-Cloud Processing (MCP) server integration using free and open-source models.
A proof-of-concept for publish design system guidance and code snippets as an MCP server for usage with LLMs
Provides documentation context to LLMs from local markdown files via MCP.
An MCP server that indexes local code into a graph database to provide context to AI assistants.
Access website documentation for AI search engines (llms.txt files) over MCP.
A server that enables LLMs to query and retrieve information from Wikipedia.
Generate and render Mermaid diagrams as images using LLMs.
Easily provide codebase context to Large Language Models (LLMs).
Memory-enhanced MCP server with local RAG database and expiring memory capabilities
Persistent memory for agents and humans. Semantic, episodic and frictionless via an automated three tier capture.