Recursive Language Model patterns for Claude Code — handle massive contexts (10M+ tokens) by treating them as external variables

MCP server that enables language models to interact with RDKit through natural language

Use any LLMs (Large Language Models) for Deep Research. Support SSE API and MCP server.

Collection of Claude Skills for DSPy framework - program language models, optimize prompts, and build RAG pipelines systematically

Model Context Protocol (MCP) server for mapping clinical terminology to Observational Medical Outcomes Partnership (OMOP) concepts using Large Language Models

AI-powered reverse engineering assistant that bridges IDA Pro with language models through MCP.

Infrastructure that connects LLMs to ERPNext. Frappe Assistant Core works with the Model Context Protocol (MCP) to expose ERPNext functionality to any compatible Language Model

A Model Context Protocol (MCP) server that provides file system context to Large Language Models (LLMs). This server enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.

SkillX: Automatically Constructing Skill Knowledge Bases for Agents

[ICML 2026] Meta Context Engineering via Agentic Skill Evolution

Learn AI and LLMs from scratch using free resources

Your First LLM-Wiki Conversation Knowledge Base

🪢 Langfuse documentation -- Langfuse is the open source LLM Engineering Platform. Observability, evals, prompt management, playground and metrics to debug and improve LLM apps

High-performance OpenAI and Anthropic compatible LLM inference server for Apple Silicon. Native MLX, continuous batching, multimodal models, MCP tool calling, and Claude Code support.

Open-source autoresearch powered by autonomous coding agents. Run Claude Code, OpenCode, and Codex with grading, shared knowledge, and multi-agent evolution. Accepted at COLM 2026.

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.

Bilingual (中文+EN) ML / LLM / diffusion / agent interview cheat sheets for AI 秋招 — generated by ARIS /interview-cheatsheet, rendered by /render-html into single-file HTML, reads anywhere — plus a CV→DBLP-fact-checked academic homepage generator and hand-authored long-form blogs 🌱