ASSUME Framework
✨An open-source agent-based simulation toolbox for European electricity markets, supporting deep reinforcement learning bidding strategies and grid congestion management modeling.
An open-source agent-based simulation toolbox for European electricity markets, supporting deep reinforcement learning bidding strategies and grid congestion management modeling.
A systematic skill library for AI Agent context management, covering fundamentals, architectural patterns, operational optimization, and evaluation systems. Compatible with Claude Code, Cursor, and other platforms. Distinguished from prompt engineering by its holistic approach to curating all information entering the model's attention budget.
An interactive open-access textbook on Machine Learning Systems engineering from Harvard University, integrating the TinyTorch framework with hands-on edge deployment labs, covering the full spectrum from ML fundamentals to system optimization.
An open-source collection of tutorials and runnable scripts for over 30 advanced Retrieval-Augmented Generation (RAG) techniques, including Graph RAG, Agentic RAG, and various retrieval optimization strategies, implemented primarily with LangChain.
A comprehensive AI engineering hub featuring 93+ production-ready projects with in-depth tutorials and implementations for LLMs, RAGs, AI Agents, and MCP, covering beginner to advanced skill levels.
An AI-driven multi-agent research assistant based on LangGraph that automates the entire research workflow from hypothesis generation, data analysis, and visualization to comprehensive report writing.
A generative agent framework inspired by human dual-process theory, combining fast and slow thinking mechanisms with in-context reinforcement learning to efficiently solve complex interactive reasoning tasks.
A curated collection of resources for Long Chain-of-Thought (Long-CoT) reasoning in LLMs, featuring papers, implementations, and datasets to track the latest advancements in the field.
A systematic collection of reading notes for LLMs top conference papers, covering motivation and method analysis in core areas like PEFT (LoRA/QLoRA), RAG, and Agents (RoleLLM), providing structured learning paths for algorithm engineers.
A curated list of must-read papers and resources on LLM Agents maintained by ZJUNLP, covering single agent capabilities (memory, planning, tool use), multi-agent collaboration, benchmarks, and mainstream development frameworks.
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